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<h1><a href="aiplatform_v1beta1.html">Vertex AI API</a> . <a href="aiplatform_v1beta1.projects.html">projects</a> . <a href="aiplatform_v1beta1.projects.locations.html">locations</a> . <a href="aiplatform_v1beta1.projects.locations.tuningJobs.html">tuningJobs</a></h1>
<h2>Instance Methods</h2>
<p class="toc_element">
  <code><a href="aiplatform_v1beta1.projects.locations.tuningJobs.operations.html">operations()</a></code>
</p>
<p class="firstline">Returns the operations Resource.</p>

<p class="toc_element">
  <code><a href="#cancel">cancel(name, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Cancels a TuningJob. Starts asynchronous cancellation on the TuningJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use GenAiTuningService.GetTuningJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the TuningJob is not deleted; instead it becomes a job with a TuningJob.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`, and TuningJob.state is set to `CANCELLED`.</p>
<p class="toc_element">
  <code><a href="#close">close()</a></code></p>
<p class="firstline">Close httplib2 connections.</p>
<p class="toc_element">
  <code><a href="#create">create(parent, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Creates a TuningJob. A created TuningJob right away will be attempted to be run.</p>
<p class="toc_element">
  <code><a href="#get">get(name, x__xgafv=None)</a></code></p>
<p class="firstline">Gets a TuningJob.</p>
<p class="toc_element">
  <code><a href="#list">list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)</a></code></p>
<p class="firstline">Lists TuningJobs in a Location.</p>
<p class="toc_element">
  <code><a href="#list_next">list_next()</a></code></p>
<p class="firstline">Retrieves the next page of results.</p>
<p class="toc_element">
  <code><a href="#optimizePrompt">optimizePrompt(parent, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Optimizes a prompt.</p>
<p class="toc_element">
  <code><a href="#rebaseTunedModel">rebaseTunedModel(parent, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Rebase a TunedModel.</p>
<h3>Method Details</h3>
<div class="method">
    <code class="details" id="cancel">cancel(name, body=None, x__xgafv=None)</code>
  <pre>Cancels a TuningJob. Starts asynchronous cancellation on the TuningJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use GenAiTuningService.GetTuningJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the TuningJob is not deleted; instead it becomes a job with a TuningJob.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`, and TuningJob.state is set to `CANCELLED`.

Args:
  name: string, Required. The name of the TuningJob to cancel. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for GenAiTuningService.CancelTuningJob.
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }
}</pre>
</div>

<div class="method">
    <code class="details" id="close">close()</code>
  <pre>Close httplib2 connections.</pre>
</div>

<div class="method">
    <code class="details" id="create">create(parent, body=None, x__xgafv=None)</code>
  <pre>Creates a TuningJob. A created TuningJob right away will be attempted to be run.

Args:
  parent: string, Required. The resource name of the Location to create the TuningJob in. Format: `projects/{project}/locations/{location}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Represents a TuningJob that runs with Google owned models.
  &quot;baseModel&quot;: &quot;A String&quot;, # The base model that is being tuned. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).
  &quot;createTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob was created.
  &quot;customBaseModel&quot;: &quot;A String&quot;, # Optional. The user-provided path to custom model weights. Set this field to tune a custom model. The path must be a Cloud Storage directory that contains the model weights in .safetensors format along with associated model metadata files. If this field is set, the base_model field must still be set to indicate which base model the custom model is derived from. This feature is only available for open source models.
  &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the TuningJob.
  &quot;distillationSpec&quot;: { # Tuning Spec for Distillation. # Tuning Spec for Distillation.
    &quot;baseTeacherModel&quot;: &quot;A String&quot;, # The base teacher model that is being distilled. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).
    &quot;hyperParameters&quot;: { # Hyperparameters for Distillation. # Optional. Hyperparameters for Distillation.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for distillation.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
    },
    &quot;pipelineRootDirectory&quot;: &quot;A String&quot;, # Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.
    &quot;studentModel&quot;: &quot;A String&quot;, # The student model that is being tuned, e.g., &quot;google/gemma-2b-1.1-it&quot;. Deprecated. Use base_model instead.
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
    &quot;tunedTeacherModelSource&quot;: &quot;A String&quot;, # The resource name of the Tuned teacher model. Format: `projects/{project}/locations/{location}/models/{model}`.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.
  },
  &quot;encryptionSpec&quot;: { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key.
    &quot;kmsKeyName&quot;: &quot;A String&quot;, # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
  },
  &quot;endTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob entered any of the following JobStates: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`, `JOB_STATE_EXPIRED`.
  &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. Only populated when job&#x27;s state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  &quot;evaluateDatasetRuns&quot;: [ # Output only. Evaluation runs for the Tuning Job.
    { # Evaluate Dataset Run Result for Tuning Job.
      &quot;checkpointId&quot;: &quot;A String&quot;, # Output only. The checkpoint id used in the evaluation run. Only populated when evaluating checkpoints.
      &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. The error of the evaluation run if any.
        &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
        &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
          },
        ],
        &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
      },
      &quot;evaluateDatasetResponse&quot;: { # Response in LRO for EvaluationService.EvaluateDataset. # Output only. Results for EvaluationService.EvaluateDataset.
        &quot;aggregationOutput&quot;: { # The aggregation result for the entire dataset and all metrics. # Output only. Aggregation statistics derived from results of EvaluationService.EvaluateDataset.
          &quot;aggregationResults&quot;: [ # One AggregationResult per metric.
            { # The aggregation result for a single metric.
              &quot;aggregationMetric&quot;: &quot;A String&quot;, # Aggregation metric.
              &quot;bleuMetricValue&quot;: { # Bleu metric value for an instance. # Results for bleu metric.
                &quot;score&quot;: 3.14, # Output only. Bleu score.
              },
              &quot;customCodeExecutionResult&quot;: { # Result for custom code execution metric. # Result for code execution metric.
                &quot;score&quot;: 3.14, # Output only. Custom code execution score.
              },
              &quot;exactMatchMetricValue&quot;: { # Exact match metric value for an instance. # Results for exact match metric.
                &quot;score&quot;: 3.14, # Output only. Exact match score.
              },
              &quot;pairwiseMetricResult&quot;: { # Spec for pairwise metric result. # Result for pairwise metric.
                &quot;customOutput&quot;: { # Spec for custom output. # Output only. Spec for custom output.
                  &quot;rawOutputs&quot;: { # Raw output. # Output only. List of raw output strings.
                    &quot;rawOutput&quot;: [ # Output only. Raw output string.
                      &quot;A String&quot;,
                    ],
                  },
                },
                &quot;explanation&quot;: &quot;A String&quot;, # Output only. Explanation for pairwise metric score.
                &quot;pairwiseChoice&quot;: &quot;A String&quot;, # Output only. Pairwise metric choice.
              },
              &quot;pointwiseMetricResult&quot;: { # Spec for pointwise metric result. # Result for pointwise metric.
                &quot;customOutput&quot;: { # Spec for custom output. # Output only. Spec for custom output.
                  &quot;rawOutputs&quot;: { # Raw output. # Output only. List of raw output strings.
                    &quot;rawOutput&quot;: [ # Output only. Raw output string.
                      &quot;A String&quot;,
                    ],
                  },
                },
                &quot;explanation&quot;: &quot;A String&quot;, # Output only. Explanation for pointwise metric score.
                &quot;score&quot;: 3.14, # Output only. Pointwise metric score.
              },
              &quot;rougeMetricValue&quot;: { # Rouge metric value for an instance. # Results for rouge metric.
                &quot;score&quot;: 3.14, # Output only. Rouge score.
              },
            },
          ],
          &quot;dataset&quot;: { # The dataset used for evaluation. # The dataset used for evaluation &amp; aggregation.
            &quot;bigquerySource&quot;: { # The BigQuery location for the input content. # BigQuery source holds the dataset.
              &quot;inputUri&quot;: &quot;A String&quot;, # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
            },
            &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # Cloud storage source holds the dataset. Currently only one Cloud Storage file path is supported.
              &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
                &quot;A String&quot;,
              ],
            },
          },
        },
        &quot;outputInfo&quot;: { # Describes the info for output of EvaluationService.EvaluateDataset. # Output only. Output info for EvaluationService.EvaluateDataset.
          &quot;gcsOutputDirectory&quot;: &quot;A String&quot;, # Output only. The full path of the Cloud Storage directory created, into which the evaluation results and aggregation results are written.
        },
      },
      &quot;operationName&quot;: &quot;A String&quot;, # Output only. The operation ID of the evaluation run. Format: `projects/{project}/locations/{location}/operations/{operation_id}`.
    },
  ],
  &quot;experiment&quot;: &quot;A String&quot;, # Output only. The Experiment associated with this TuningJob.
  &quot;fullFineTuningSpec&quot;: { # Tuning Spec for Full Fine Tuning. # Tuning Spec for Full Fine Tuning.
    &quot;hyperParameters&quot;: { # Hyperparameters for SFT. # Optional. Hyperparameters for Full Fine Tuning.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for tuning.
      &quot;batchSize&quot;: &quot;A String&quot;, # Optional. Batch size for tuning. This feature is only available for open source models.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRate&quot;: 3.14, # Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
  },
  &quot;labels&quot;: { # Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
    &quot;a_key&quot;: &quot;A String&quot;,
  },
  &quot;name&quot;: &quot;A String&quot;, # Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`
  &quot;outputUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to the directory where tuning job outputs are written to. This field is only available and required for open source models.
  &quot;partnerModelTuningSpec&quot;: { # Tuning spec for Partner models. # Tuning Spec for open sourced and third party Partner models.
    &quot;hyperParameters&quot;: { # Hyperparameters for tuning. The accepted hyper_parameters and their valid range of values will differ depending on the base model.
      &quot;a_key&quot;: &quot;&quot;,
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.
  },
  &quot;pipelineJob&quot;: &quot;A String&quot;, # Output only. The resource name of the PipelineJob associated with the TuningJob. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}`.
  &quot;preTunedModel&quot;: { # A pre-tuned model for continuous tuning. # The pre-tuned model for continuous tuning.
    &quot;baseModel&quot;: &quot;A String&quot;, # Output only. The name of the base model this PreTunedModel was tuned from.
    &quot;checkpointId&quot;: &quot;A String&quot;, # Optional. The source checkpoint id. If not specified, the default checkpoint will be used.
    &quot;tunedModelName&quot;: &quot;A String&quot;, # The resource name of the Model. E.g., a model resource name with a specified version id or alias: `projects/{project}/locations/{location}/models/{model}@{version_id}` `projects/{project}/locations/{location}/models/{model}@{alias}` Or, omit the version id to use the default version: `projects/{project}/locations/{location}/models/{model}`
  },
  &quot;preferenceOptimizationSpec&quot;: { # Tuning Spec for Preference Optimization. # Tuning Spec for Preference Optimization.
    &quot;exportLastCheckpointOnly&quot;: True or False, # Optional. If set to true, disable intermediate checkpoints for Preference Optimization and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for Preference Optimization. Default is false.
    &quot;hyperParameters&quot;: { # Hyperparameters for Preference Optimization. # Optional. Hyperparameters for Preference Optimization.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for preference optimization.
      &quot;beta&quot;: 3.14, # Optional. Weight for KL Divergence regularization.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.
  },
  &quot;satisfiesPzi&quot;: True or False, # Output only. Reserved for future use.
  &quot;satisfiesPzs&quot;: True or False, # Output only. Reserved for future use.
  &quot;serviceAccount&quot;: &quot;A String&quot;, # The service account that the tuningJob workload runs as. If not specified, the Vertex AI Secure Fine-Tuned Service Agent in the project will be used. See https://cloud.google.com/iam/docs/service-agents#vertex-ai-secure-fine-tuning-service-agent Users starting the pipeline must have the `iam.serviceAccounts.actAs` permission on this service account.
  &quot;startTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state.
  &quot;state&quot;: &quot;A String&quot;, # Output only. The detailed state of the job.
  &quot;supervisedTuningSpec&quot;: { # Tuning Spec for Supervised Tuning for first party models. # Tuning Spec for Supervised Fine Tuning.
    &quot;evaluationConfig&quot;: { # Evaluation Config for Tuning Job. # Optional. Evaluation Config for Tuning Job.
      &quot;autoraterConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config for evaluation.
        &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
        &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
        &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
          &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
          &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
          &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
          &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
          &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
            &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
            &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
              &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
              &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
            },
            &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
          },
          &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
          &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
          &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
          &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
            &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
          },
          &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
          &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
          &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
          &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
          &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
            &quot;A String&quot;,
          ],
          &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
            &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
            &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
              # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            ],
            &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
            &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
              &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            },
            &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
            &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
              &quot;A String&quot;,
            ],
            &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
            &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
            &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
            &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
            &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
            &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
            &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
            &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
            &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
            &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
            &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
            &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
            &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
            &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
              &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            },
            &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
              &quot;A String&quot;,
            ],
            &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
            &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
              &quot;A String&quot;,
            ],
            &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
            &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
          },
          &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
            &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
              &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
            },
            &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
              &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
            },
          },
          &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
          &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
            &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
            &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
              &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                { # Configuration for a single speaker in a multi-speaker setup.
                  &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                  &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                    &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                      &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                    },
                  },
                },
              ],
            },
            &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
              &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
              },
            },
          },
          &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
            &quot;A String&quot;,
          ],
          &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
          &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
            &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
            &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
          },
          &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
          &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
        },
        &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
      },
      &quot;metrics&quot;: [ # Required. The metrics used for evaluation.
        { # The metric used for running evaluations.
          &quot;aggregationMetrics&quot;: [ # Optional. The aggregation metrics to use.
            &quot;A String&quot;,
          ],
          &quot;bleuSpec&quot;: { # Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1. # Spec for bleu metric.
            &quot;useEffectiveOrder&quot;: True or False, # Optional. Whether to use_effective_order to compute bleu score.
          },
          &quot;customCodeExecutionSpec&quot;: { # Specificies a metric that is populated by evaluating user-defined Python code. # Spec for Custom Code Execution metric.
            &quot;evaluationFunction&quot;: &quot;A String&quot;, # Required. Python function. Expected user to define the following function, e.g.: def evaluate(instance: dict[str, Any]) -&gt; float: Please include this function signature in the code snippet. Instance is the evaluation instance, any fields populated in the instance are available to the function as instance[field_name]. Example: Example input: ``` instance= EvaluationInstance( response=EvaluationInstance.InstanceData(text=&quot;The answer is 4.&quot;), reference=EvaluationInstance.InstanceData(text=&quot;4&quot;) ) ``` Example converted input: ``` { &#x27;response&#x27;: {&#x27;text&#x27;: &#x27;The answer is 4.&#x27;}, &#x27;reference&#x27;: {&#x27;text&#x27;: &#x27;4&#x27;} } ``` Example python function: ``` def evaluate(instance: dict[str, Any]) -&gt; float: if instance&#x27;response&#x27; == instance&#x27;reference&#x27;: return 1.0 return 0.0 ```
          },
          &quot;exactMatchSpec&quot;: { # Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0. # Spec for exact match metric.
          },
          &quot;llmBasedMetricSpec&quot;: { # Specification for an LLM based metric. # Spec for an LLM based metric.
            &quot;additionalConfig&quot;: { # Optional. Optional additional configuration for the metric.
              &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
            },
            &quot;judgeAutoraterConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Optional configuration for the judge LLM (Autorater).
              &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
              &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
              &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
                &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
                &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
                &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
                &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
                &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                  &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                  &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                    &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                  },
                  &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
                },
                &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
                &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
                &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
                &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                  &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
                },
                &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
                &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
                &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
                &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
                &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                  &quot;A String&quot;,
                ],
                &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                  &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                  &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                    # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  ],
                  &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                  &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                    &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                  &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                    &quot;A String&quot;,
                  ],
                  &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                  &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                  &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                  &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                  &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                  &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                  &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                  &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                  &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                  &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                  &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                  &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                  &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                  &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                    &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                    &quot;A String&quot;,
                  ],
                  &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                  &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                    &quot;A String&quot;,
                  ],
                  &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                  &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
                },
                &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                  &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                    &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                  },
                  &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                    &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                  },
                },
                &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
                &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                  &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                  &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                    &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                      { # Configuration for a single speaker in a multi-speaker setup.
                        &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                        &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                          &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                            &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                          },
                        },
                      },
                    ],
                  },
                  &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                    &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                      &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                    },
                  },
                },
                &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                  &quot;A String&quot;,
                ],
                &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
                &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                  &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                  &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
                },
                &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
                &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
              },
              &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
            },
            &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Template for the prompt sent to the judge model.
            &quot;predefinedRubricGenerationSpec&quot;: { # The spec for a pre-defined metric. # Dynamically generate rubrics using a predefined spec.
              &quot;metricSpecName&quot;: &quot;A String&quot;, # Required. The name of a pre-defined metric, such as &quot;instruction_following_v1&quot; or &quot;text_quality_v1&quot;.
              &quot;metricSpecParameters&quot;: { # Optional. The parameters needed to run the pre-defined metric.
                &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
              },
            },
            &quot;rubricGenerationSpec&quot;: { # Specification for how rubrics should be generated. # Dynamically generate rubrics using this specification.
              &quot;modelConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Configuration for the model used in rubric generation. Configs including sampling count and base model can be specified here. Flipping is not supported for rubric generation.
                &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
                &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
                &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
                  &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
                  &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
                  &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
                  &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
                  &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                    &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                    &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                      &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                    },
                    &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
                  },
                  &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
                  &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
                  &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
                  &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                    &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
                  },
                  &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
                  &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
                  &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
                  &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
                  &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                    &quot;A String&quot;,
                  ],
                  &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                    &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                    &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                      # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    ],
                    &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                    &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                      &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    },
                    &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                    &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                      &quot;A String&quot;,
                    ],
                    &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                    &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                    &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                    &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                    &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                    &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                    &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                    &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                    &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                    &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                    &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                    &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                    &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                    &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                      &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    },
                    &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                      &quot;A String&quot;,
                    ],
                    &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                    &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                      &quot;A String&quot;,
                    ],
                    &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                    &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
                  },
                  &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                    &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                      &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                    },
                    &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                      &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                    },
                  },
                  &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
                  &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                    &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                    &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                      &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                        { # Configuration for a single speaker in a multi-speaker setup.
                          &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                          &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                            &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                              &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                            },
                          },
                        },
                      ],
                    },
                    &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                      &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                        &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                      },
                    },
                  },
                  &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                    &quot;A String&quot;,
                  ],
                  &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
                  &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                    &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                    &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
                  },
                  &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
                  &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
                },
                &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
              },
              &quot;promptTemplate&quot;: &quot;A String&quot;, # Template for the prompt used to generate rubrics. The details should be updated based on the most-recent recipe requirements.
              &quot;rubricContentType&quot;: &quot;A String&quot;, # The type of rubric content to be generated.
              &quot;rubricTypeOntology&quot;: [ # Optional. An optional, pre-defined list of allowed types for generated rubrics. If this field is provided, it implies `include_rubric_type` should be true, and the generated rubric types should be chosen from this ontology.
                &quot;A String&quot;,
              ],
            },
            &quot;rubricGroupKey&quot;: &quot;A String&quot;, # Use a pre-defined group of rubrics associated with the input. Refers to a key in the rubric_groups map of EvaluationInstance.
            &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for the judge model.
          },
          &quot;pairwiseMetricSpec&quot;: { # Spec for pairwise metric. # Spec for pairwise metric.
            &quot;baselineResponseFieldName&quot;: &quot;A String&quot;, # Optional. The field name of the baseline response.
            &quot;candidateResponseFieldName&quot;: &quot;A String&quot;, # Optional. The field name of the candidate response.
            &quot;customOutputFormatConfig&quot;: { # Spec for custom output format configuration. # Optional. CustomOutputFormatConfig allows customization of metric output. When this config is set, the default output is replaced with the raw output string. If a custom format is chosen, the `pairwise_choice` and `explanation` fields in the corresponding metric result will be empty.
              &quot;returnRawOutput&quot;: True or False, # Optional. Whether to return raw output.
            },
            &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Metric prompt template for pairwise metric.
            &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for pairwise metric.
          },
          &quot;pointwiseMetricSpec&quot;: { # Spec for pointwise metric. # Spec for pointwise metric.
            &quot;customOutputFormatConfig&quot;: { # Spec for custom output format configuration. # Optional. CustomOutputFormatConfig allows customization of metric output. By default, metrics return a score and explanation. When this config is set, the default output is replaced with either: - The raw output string. - A parsed output based on a user-defined schema. If a custom format is chosen, the `score` and `explanation` fields in the corresponding metric result will be empty.
              &quot;returnRawOutput&quot;: True or False, # Optional. Whether to return raw output.
            },
            &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Metric prompt template for pointwise metric.
            &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for pointwise metric.
          },
          &quot;predefinedMetricSpec&quot;: { # The spec for a pre-defined metric. # The spec for a pre-defined metric.
            &quot;metricSpecName&quot;: &quot;A String&quot;, # Required. The name of a pre-defined metric, such as &quot;instruction_following_v1&quot; or &quot;text_quality_v1&quot;.
            &quot;metricSpecParameters&quot;: { # Optional. The parameters needed to run the pre-defined metric.
              &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
            },
          },
          &quot;rougeSpec&quot;: { # Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1. # Spec for rouge metric.
            &quot;rougeType&quot;: &quot;A String&quot;, # Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.
            &quot;splitSummaries&quot;: True or False, # Optional. Whether to split summaries while using rougeLsum.
            &quot;useStemmer&quot;: True or False, # Optional. Whether to use stemmer to compute rouge score.
          },
        },
      ],
      &quot;outputConfig&quot;: { # Config for evaluation output. # Required. Config for evaluation output.
        &quot;gcsDestination&quot;: { # The Google Cloud Storage location where the output is to be written to. # Cloud storage destination for evaluation output.
          &quot;outputUriPrefix&quot;: &quot;A String&quot;, # Required. Google Cloud Storage URI to output directory. If the uri doesn&#x27;t end with &#x27;/&#x27;, a &#x27;/&#x27; will be automatically appended. The directory is created if it doesn&#x27;t exist.
        },
      },
    },
    &quot;exportLastCheckpointOnly&quot;: True or False, # Optional. If set to true, disable intermediate checkpoints for SFT and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for SFT. Default is false.
    &quot;hyperParameters&quot;: { # Hyperparameters for SFT. # Optional. Hyperparameters for SFT.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for tuning.
      &quot;batchSize&quot;: &quot;A String&quot;, # Optional. Batch size for tuning. This feature is only available for open source models.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRate&quot;: 3.14, # Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    &quot;tuningMode&quot;: &quot;A String&quot;, # Tuning mode.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
  },
  &quot;tunedModel&quot;: { # The Model Registry Model and Online Prediction Endpoint associated with this TuningJob. # Output only. The tuned model resources associated with this TuningJob.
    &quot;checkpoints&quot;: [ # Output only. The checkpoints associated with this TunedModel. This field is only populated for tuning jobs that enable intermediate checkpoints.
      { # TunedModelCheckpoint for the Tuned Model of a Tuning Job.
        &quot;checkpointId&quot;: &quot;A String&quot;, # The ID of the checkpoint.
        &quot;endpoint&quot;: &quot;A String&quot;, # The Endpoint resource name that the checkpoint is deployed to. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.
        &quot;epoch&quot;: &quot;A String&quot;, # The epoch of the checkpoint.
        &quot;step&quot;: &quot;A String&quot;, # The step of the checkpoint.
      },
    ],
    &quot;endpoint&quot;: &quot;A String&quot;, # Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.
    &quot;model&quot;: &quot;A String&quot;, # Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}@{version_id}` When tuning from a base model, the version ID will be 1. For continuous tuning, if the provided tuned_model_display_name is set and different from parent model&#x27;s display name, the tuned model will have a new parent model with version 1. Otherwise the version id will be incremented by 1 from the last version ID in the parent model. E.g., `projects/{project}/locations/{location}/models/{model}@{last_version_id + 1}`
  },
  &quot;tunedModelDisplayName&quot;: &quot;A String&quot;, # Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters. For continuous tuning, tuned_model_display_name will by default use the same display name as the pre-tuned model. If a new display name is provided, the tuning job will create a new model instead of a new version.
  &quot;tuningDataStats&quot;: { # The tuning data statistic values for TuningJob. # Output only. The tuning data statistics associated with this TuningJob.
    &quot;distillationDataStats&quot;: { # Statistics computed for datasets used for distillation. # Output only. Statistics for distillation.
      &quot;trainingDatasetStats&quot;: { # Statistics computed over a tuning dataset. # Output only. Statistics computed for the training dataset.
        &quot;droppedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
          &quot;A String&quot;,
        ],
        &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `dropped_example_indices`, the user-facing reason why the example was dropped.
          &quot;A String&quot;,
        ],
        &quot;totalBillableCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of billable characters in the tuning dataset.
        &quot;totalTuningCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of tuning characters in the tuning dataset.
        &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
        &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
        &quot;userDatasetExamples&quot;: [ # Output only. Sample user messages in the training dataset uri.
          { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
            &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
              { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                  &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                  &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                },
                &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                  &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                  &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                },
                &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                  &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                  &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                  &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                },
                &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                  &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                    &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                  },
                  &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                  &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                },
                &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                  &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                  &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                  &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                    { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                      &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                        &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                      &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                        &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                    },
                  ],
                  &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                    &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                  },
                },
                &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                  &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                  &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                  &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                },
                &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                  &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                  &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                  &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                },
              },
            ],
            &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
          },
        ],
        &quot;userInputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user input tokens.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
        &quot;userMessagePerExampleDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the messages per example.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
        &quot;userOutputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user output tokens.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
      },
    },
    &quot;preferenceOptimizationDataStats&quot;: { # Statistics computed for datasets used for preference optimization. # Output only. Statistics for preference optimization.
      &quot;droppedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
        &quot;A String&quot;,
      ],
      &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `dropped_example_indices`, the user-facing reason why the example was dropped.
        &quot;A String&quot;,
      ],
      &quot;scoreVariancePerExampleDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for scores variance per example.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
      &quot;scoresDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for scores.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
      &quot;totalBillableTokenCount&quot;: &quot;A String&quot;, # Output only. Number of billable tokens in the tuning dataset.
      &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
      &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
      &quot;userDatasetExamples&quot;: [ # Output only. Sample user examples in the training dataset.
        { # Input example for preference optimization.
          &quot;completions&quot;: [ # List of completions for a given prompt.
            { # Completion and its preference score.
              &quot;completion&quot;: { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message. # Single turn completion for the given prompt.
                &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                  { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                    &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                      &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                      &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                    },
                    &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                      &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                      &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                    },
                    &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                      &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                      &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                        &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                      },
                      &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                      &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                    },
                    &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                      &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                      &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                      &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                        { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                          &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                            &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                            &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                            &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                          },
                          &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                            &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                            &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                            &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                          },
                        },
                      ],
                      &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                        &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                      },
                    },
                    &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                      &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                    &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                    &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                    &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                      &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                      &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                      &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                    },
                  },
                ],
                &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
              },
              &quot;score&quot;: 3.14, # The score for the given completion.
            },
          ],
          &quot;contents&quot;: [ # Multi-turn contents that represents the Prompt.
            { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
              &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                  &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                    &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                    &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                  },
                  &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                    &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                    &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                  },
                  &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                    &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                    &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                  },
                  &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                    &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                      &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                    },
                    &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                    &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                  },
                  &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                    &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                    &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                    &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                      { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                        &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                          &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                        &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                          &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                      },
                    ],
                    &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                      &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                    },
                  },
                  &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                    &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                    &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                  },
                  &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                  &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                  &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                  &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                    &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                    &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                    &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                  },
                },
              ],
              &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
            },
          ],
        },
      ],
      &quot;userInputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user input tokens.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
      &quot;userOutputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user output tokens.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
    },
    &quot;supervisedTuningDataStats&quot;: { # Tuning data statistics for Supervised Tuning. # The SFT Tuning data stats.
      &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `truncated_example_indices`, the user-facing reason why the example was dropped.
        &quot;A String&quot;,
      ],
      &quot;totalBillableCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of billable characters in the tuning dataset.
      &quot;totalBillableTokenCount&quot;: &quot;A String&quot;, # Output only. Number of billable tokens in the tuning dataset.
      &quot;totalTruncatedExampleCount&quot;: &quot;A String&quot;, # Output only. The number of examples in the dataset that have been dropped. An example can be dropped for reasons including: too many tokens, contains an invalid image, contains too many images, etc.
      &quot;totalTuningCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of tuning characters in the tuning dataset.
      &quot;truncatedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
        &quot;A String&quot;,
      ],
      &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
      &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
      &quot;userDatasetExamples&quot;: [ # Output only. Sample user messages in the training dataset uri.
        { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
          &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
            { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
              &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
              },
              &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
              },
              &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
              },
              &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                  &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                },
                &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
              },
              &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                  { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                    &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                      &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                      &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                  },
                ],
                &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                  &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                },
              },
              &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
              },
              &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
              &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
              &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
              &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
              },
            },
          ],
          &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
        },
      ],
      &quot;userInputTokenDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user input tokens.
        &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
      },
      &quot;userMessagePerExampleDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the messages per example.
        &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
      },
      &quot;userOutputTokenDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user output tokens.
        &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
      },
    },
  },
  &quot;tuningJobState&quot;: &quot;A String&quot;, # Output only. The detail state of the tuning job (while the overall `JobState` is running).
  &quot;updateTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob was most recently updated.
  &quot;veoTuningSpec&quot;: { # Tuning Spec for Veo Model Tuning. # Tuning Spec for Veo Tuning.
    &quot;hyperParameters&quot;: { # Hyperparameters for Veo. # Optional. Hyperparameters for Veo.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
      &quot;tuningTask&quot;: &quot;A String&quot;, # Optional. The tuning task. Either I2V or T2V.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
  },
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Represents a TuningJob that runs with Google owned models.
  &quot;baseModel&quot;: &quot;A String&quot;, # The base model that is being tuned. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).
  &quot;createTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob was created.
  &quot;customBaseModel&quot;: &quot;A String&quot;, # Optional. The user-provided path to custom model weights. Set this field to tune a custom model. The path must be a Cloud Storage directory that contains the model weights in .safetensors format along with associated model metadata files. If this field is set, the base_model field must still be set to indicate which base model the custom model is derived from. This feature is only available for open source models.
  &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the TuningJob.
  &quot;distillationSpec&quot;: { # Tuning Spec for Distillation. # Tuning Spec for Distillation.
    &quot;baseTeacherModel&quot;: &quot;A String&quot;, # The base teacher model that is being distilled. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).
    &quot;hyperParameters&quot;: { # Hyperparameters for Distillation. # Optional. Hyperparameters for Distillation.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for distillation.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
    },
    &quot;pipelineRootDirectory&quot;: &quot;A String&quot;, # Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.
    &quot;studentModel&quot;: &quot;A String&quot;, # The student model that is being tuned, e.g., &quot;google/gemma-2b-1.1-it&quot;. Deprecated. Use base_model instead.
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
    &quot;tunedTeacherModelSource&quot;: &quot;A String&quot;, # The resource name of the Tuned teacher model. Format: `projects/{project}/locations/{location}/models/{model}`.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.
  },
  &quot;encryptionSpec&quot;: { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key.
    &quot;kmsKeyName&quot;: &quot;A String&quot;, # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
  },
  &quot;endTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob entered any of the following JobStates: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`, `JOB_STATE_EXPIRED`.
  &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. Only populated when job&#x27;s state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  &quot;evaluateDatasetRuns&quot;: [ # Output only. Evaluation runs for the Tuning Job.
    { # Evaluate Dataset Run Result for Tuning Job.
      &quot;checkpointId&quot;: &quot;A String&quot;, # Output only. The checkpoint id used in the evaluation run. Only populated when evaluating checkpoints.
      &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. The error of the evaluation run if any.
        &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
        &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
          },
        ],
        &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
      },
      &quot;evaluateDatasetResponse&quot;: { # Response in LRO for EvaluationService.EvaluateDataset. # Output only. Results for EvaluationService.EvaluateDataset.
        &quot;aggregationOutput&quot;: { # The aggregation result for the entire dataset and all metrics. # Output only. Aggregation statistics derived from results of EvaluationService.EvaluateDataset.
          &quot;aggregationResults&quot;: [ # One AggregationResult per metric.
            { # The aggregation result for a single metric.
              &quot;aggregationMetric&quot;: &quot;A String&quot;, # Aggregation metric.
              &quot;bleuMetricValue&quot;: { # Bleu metric value for an instance. # Results for bleu metric.
                &quot;score&quot;: 3.14, # Output only. Bleu score.
              },
              &quot;customCodeExecutionResult&quot;: { # Result for custom code execution metric. # Result for code execution metric.
                &quot;score&quot;: 3.14, # Output only. Custom code execution score.
              },
              &quot;exactMatchMetricValue&quot;: { # Exact match metric value for an instance. # Results for exact match metric.
                &quot;score&quot;: 3.14, # Output only. Exact match score.
              },
              &quot;pairwiseMetricResult&quot;: { # Spec for pairwise metric result. # Result for pairwise metric.
                &quot;customOutput&quot;: { # Spec for custom output. # Output only. Spec for custom output.
                  &quot;rawOutputs&quot;: { # Raw output. # Output only. List of raw output strings.
                    &quot;rawOutput&quot;: [ # Output only. Raw output string.
                      &quot;A String&quot;,
                    ],
                  },
                },
                &quot;explanation&quot;: &quot;A String&quot;, # Output only. Explanation for pairwise metric score.
                &quot;pairwiseChoice&quot;: &quot;A String&quot;, # Output only. Pairwise metric choice.
              },
              &quot;pointwiseMetricResult&quot;: { # Spec for pointwise metric result. # Result for pointwise metric.
                &quot;customOutput&quot;: { # Spec for custom output. # Output only. Spec for custom output.
                  &quot;rawOutputs&quot;: { # Raw output. # Output only. List of raw output strings.
                    &quot;rawOutput&quot;: [ # Output only. Raw output string.
                      &quot;A String&quot;,
                    ],
                  },
                },
                &quot;explanation&quot;: &quot;A String&quot;, # Output only. Explanation for pointwise metric score.
                &quot;score&quot;: 3.14, # Output only. Pointwise metric score.
              },
              &quot;rougeMetricValue&quot;: { # Rouge metric value for an instance. # Results for rouge metric.
                &quot;score&quot;: 3.14, # Output only. Rouge score.
              },
            },
          ],
          &quot;dataset&quot;: { # The dataset used for evaluation. # The dataset used for evaluation &amp; aggregation.
            &quot;bigquerySource&quot;: { # The BigQuery location for the input content. # BigQuery source holds the dataset.
              &quot;inputUri&quot;: &quot;A String&quot;, # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
            },
            &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # Cloud storage source holds the dataset. Currently only one Cloud Storage file path is supported.
              &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
                &quot;A String&quot;,
              ],
            },
          },
        },
        &quot;outputInfo&quot;: { # Describes the info for output of EvaluationService.EvaluateDataset. # Output only. Output info for EvaluationService.EvaluateDataset.
          &quot;gcsOutputDirectory&quot;: &quot;A String&quot;, # Output only. The full path of the Cloud Storage directory created, into which the evaluation results and aggregation results are written.
        },
      },
      &quot;operationName&quot;: &quot;A String&quot;, # Output only. The operation ID of the evaluation run. Format: `projects/{project}/locations/{location}/operations/{operation_id}`.
    },
  ],
  &quot;experiment&quot;: &quot;A String&quot;, # Output only. The Experiment associated with this TuningJob.
  &quot;fullFineTuningSpec&quot;: { # Tuning Spec for Full Fine Tuning. # Tuning Spec for Full Fine Tuning.
    &quot;hyperParameters&quot;: { # Hyperparameters for SFT. # Optional. Hyperparameters for Full Fine Tuning.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for tuning.
      &quot;batchSize&quot;: &quot;A String&quot;, # Optional. Batch size for tuning. This feature is only available for open source models.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRate&quot;: 3.14, # Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
  },
  &quot;labels&quot;: { # Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
    &quot;a_key&quot;: &quot;A String&quot;,
  },
  &quot;name&quot;: &quot;A String&quot;, # Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`
  &quot;outputUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to the directory where tuning job outputs are written to. This field is only available and required for open source models.
  &quot;partnerModelTuningSpec&quot;: { # Tuning spec for Partner models. # Tuning Spec for open sourced and third party Partner models.
    &quot;hyperParameters&quot;: { # Hyperparameters for tuning. The accepted hyper_parameters and their valid range of values will differ depending on the base model.
      &quot;a_key&quot;: &quot;&quot;,
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.
  },
  &quot;pipelineJob&quot;: &quot;A String&quot;, # Output only. The resource name of the PipelineJob associated with the TuningJob. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}`.
  &quot;preTunedModel&quot;: { # A pre-tuned model for continuous tuning. # The pre-tuned model for continuous tuning.
    &quot;baseModel&quot;: &quot;A String&quot;, # Output only. The name of the base model this PreTunedModel was tuned from.
    &quot;checkpointId&quot;: &quot;A String&quot;, # Optional. The source checkpoint id. If not specified, the default checkpoint will be used.
    &quot;tunedModelName&quot;: &quot;A String&quot;, # The resource name of the Model. E.g., a model resource name with a specified version id or alias: `projects/{project}/locations/{location}/models/{model}@{version_id}` `projects/{project}/locations/{location}/models/{model}@{alias}` Or, omit the version id to use the default version: `projects/{project}/locations/{location}/models/{model}`
  },
  &quot;preferenceOptimizationSpec&quot;: { # Tuning Spec for Preference Optimization. # Tuning Spec for Preference Optimization.
    &quot;exportLastCheckpointOnly&quot;: True or False, # Optional. If set to true, disable intermediate checkpoints for Preference Optimization and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for Preference Optimization. Default is false.
    &quot;hyperParameters&quot;: { # Hyperparameters for Preference Optimization. # Optional. Hyperparameters for Preference Optimization.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for preference optimization.
      &quot;beta&quot;: 3.14, # Optional. Weight for KL Divergence regularization.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.
  },
  &quot;satisfiesPzi&quot;: True or False, # Output only. Reserved for future use.
  &quot;satisfiesPzs&quot;: True or False, # Output only. Reserved for future use.
  &quot;serviceAccount&quot;: &quot;A String&quot;, # The service account that the tuningJob workload runs as. If not specified, the Vertex AI Secure Fine-Tuned Service Agent in the project will be used. See https://cloud.google.com/iam/docs/service-agents#vertex-ai-secure-fine-tuning-service-agent Users starting the pipeline must have the `iam.serviceAccounts.actAs` permission on this service account.
  &quot;startTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state.
  &quot;state&quot;: &quot;A String&quot;, # Output only. The detailed state of the job.
  &quot;supervisedTuningSpec&quot;: { # Tuning Spec for Supervised Tuning for first party models. # Tuning Spec for Supervised Fine Tuning.
    &quot;evaluationConfig&quot;: { # Evaluation Config for Tuning Job. # Optional. Evaluation Config for Tuning Job.
      &quot;autoraterConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config for evaluation.
        &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
        &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
        &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
          &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
          &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
          &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
          &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
          &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
            &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
            &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
              &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
              &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
            },
            &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
          },
          &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
          &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
          &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
          &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
            &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
          },
          &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
          &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
          &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
          &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
          &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
            &quot;A String&quot;,
          ],
          &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
            &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
            &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
              # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            ],
            &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
            &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
              &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            },
            &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
            &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
              &quot;A String&quot;,
            ],
            &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
            &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
            &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
            &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
            &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
            &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
            &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
            &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
            &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
            &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
            &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
            &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
            &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
            &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
              &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            },
            &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
              &quot;A String&quot;,
            ],
            &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
            &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
              &quot;A String&quot;,
            ],
            &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
            &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
          },
          &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
            &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
              &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
            },
            &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
              &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
            },
          },
          &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
          &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
            &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
            &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
              &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                { # Configuration for a single speaker in a multi-speaker setup.
                  &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                  &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                    &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                      &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                    },
                  },
                },
              ],
            },
            &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
              &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
              },
            },
          },
          &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
            &quot;A String&quot;,
          ],
          &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
          &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
            &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
            &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
          },
          &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
          &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
        },
        &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
      },
      &quot;metrics&quot;: [ # Required. The metrics used for evaluation.
        { # The metric used for running evaluations.
          &quot;aggregationMetrics&quot;: [ # Optional. The aggregation metrics to use.
            &quot;A String&quot;,
          ],
          &quot;bleuSpec&quot;: { # Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1. # Spec for bleu metric.
            &quot;useEffectiveOrder&quot;: True or False, # Optional. Whether to use_effective_order to compute bleu score.
          },
          &quot;customCodeExecutionSpec&quot;: { # Specificies a metric that is populated by evaluating user-defined Python code. # Spec for Custom Code Execution metric.
            &quot;evaluationFunction&quot;: &quot;A String&quot;, # Required. Python function. Expected user to define the following function, e.g.: def evaluate(instance: dict[str, Any]) -&gt; float: Please include this function signature in the code snippet. Instance is the evaluation instance, any fields populated in the instance are available to the function as instance[field_name]. Example: Example input: ``` instance= EvaluationInstance( response=EvaluationInstance.InstanceData(text=&quot;The answer is 4.&quot;), reference=EvaluationInstance.InstanceData(text=&quot;4&quot;) ) ``` Example converted input: ``` { &#x27;response&#x27;: {&#x27;text&#x27;: &#x27;The answer is 4.&#x27;}, &#x27;reference&#x27;: {&#x27;text&#x27;: &#x27;4&#x27;} } ``` Example python function: ``` def evaluate(instance: dict[str, Any]) -&gt; float: if instance&#x27;response&#x27; == instance&#x27;reference&#x27;: return 1.0 return 0.0 ```
          },
          &quot;exactMatchSpec&quot;: { # Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0. # Spec for exact match metric.
          },
          &quot;llmBasedMetricSpec&quot;: { # Specification for an LLM based metric. # Spec for an LLM based metric.
            &quot;additionalConfig&quot;: { # Optional. Optional additional configuration for the metric.
              &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
            },
            &quot;judgeAutoraterConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Optional configuration for the judge LLM (Autorater).
              &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
              &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
              &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
                &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
                &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
                &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
                &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
                &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                  &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                  &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                    &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                  },
                  &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
                },
                &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
                &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
                &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
                &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                  &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
                },
                &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
                &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
                &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
                &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
                &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                  &quot;A String&quot;,
                ],
                &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                  &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                  &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                    # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  ],
                  &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                  &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                    &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                  &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                    &quot;A String&quot;,
                  ],
                  &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                  &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                  &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                  &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                  &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                  &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                  &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                  &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                  &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                  &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                  &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                  &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                  &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                  &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                    &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                    &quot;A String&quot;,
                  ],
                  &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                  &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                    &quot;A String&quot;,
                  ],
                  &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                  &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
                },
                &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                  &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                    &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                  },
                  &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                    &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                  },
                },
                &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
                &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                  &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                  &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                    &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                      { # Configuration for a single speaker in a multi-speaker setup.
                        &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                        &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                          &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                            &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                          },
                        },
                      },
                    ],
                  },
                  &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                    &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                      &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                    },
                  },
                },
                &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                  &quot;A String&quot;,
                ],
                &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
                &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                  &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                  &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
                },
                &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
                &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
              },
              &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
            },
            &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Template for the prompt sent to the judge model.
            &quot;predefinedRubricGenerationSpec&quot;: { # The spec for a pre-defined metric. # Dynamically generate rubrics using a predefined spec.
              &quot;metricSpecName&quot;: &quot;A String&quot;, # Required. The name of a pre-defined metric, such as &quot;instruction_following_v1&quot; or &quot;text_quality_v1&quot;.
              &quot;metricSpecParameters&quot;: { # Optional. The parameters needed to run the pre-defined metric.
                &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
              },
            },
            &quot;rubricGenerationSpec&quot;: { # Specification for how rubrics should be generated. # Dynamically generate rubrics using this specification.
              &quot;modelConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Configuration for the model used in rubric generation. Configs including sampling count and base model can be specified here. Flipping is not supported for rubric generation.
                &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
                &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
                &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
                  &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
                  &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
                  &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
                  &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
                  &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                    &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                    &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                      &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                    },
                    &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
                  },
                  &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
                  &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
                  &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
                  &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                    &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
                  },
                  &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
                  &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
                  &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
                  &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
                  &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                    &quot;A String&quot;,
                  ],
                  &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                    &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                    &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                      # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    ],
                    &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                    &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                      &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    },
                    &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                    &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                      &quot;A String&quot;,
                    ],
                    &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                    &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                    &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                    &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                    &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                    &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                    &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                    &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                    &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                    &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                    &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                    &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                    &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                    &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                      &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    },
                    &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                      &quot;A String&quot;,
                    ],
                    &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                    &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                      &quot;A String&quot;,
                    ],
                    &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                    &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
                  },
                  &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                    &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                      &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                    },
                    &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                      &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                    },
                  },
                  &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
                  &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                    &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                    &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                      &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                        { # Configuration for a single speaker in a multi-speaker setup.
                          &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                          &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                            &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                              &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                            },
                          },
                        },
                      ],
                    },
                    &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                      &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                        &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                      },
                    },
                  },
                  &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                    &quot;A String&quot;,
                  ],
                  &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
                  &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                    &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                    &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
                  },
                  &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
                  &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
                },
                &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
              },
              &quot;promptTemplate&quot;: &quot;A String&quot;, # Template for the prompt used to generate rubrics. The details should be updated based on the most-recent recipe requirements.
              &quot;rubricContentType&quot;: &quot;A String&quot;, # The type of rubric content to be generated.
              &quot;rubricTypeOntology&quot;: [ # Optional. An optional, pre-defined list of allowed types for generated rubrics. If this field is provided, it implies `include_rubric_type` should be true, and the generated rubric types should be chosen from this ontology.
                &quot;A String&quot;,
              ],
            },
            &quot;rubricGroupKey&quot;: &quot;A String&quot;, # Use a pre-defined group of rubrics associated with the input. Refers to a key in the rubric_groups map of EvaluationInstance.
            &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for the judge model.
          },
          &quot;pairwiseMetricSpec&quot;: { # Spec for pairwise metric. # Spec for pairwise metric.
            &quot;baselineResponseFieldName&quot;: &quot;A String&quot;, # Optional. The field name of the baseline response.
            &quot;candidateResponseFieldName&quot;: &quot;A String&quot;, # Optional. The field name of the candidate response.
            &quot;customOutputFormatConfig&quot;: { # Spec for custom output format configuration. # Optional. CustomOutputFormatConfig allows customization of metric output. When this config is set, the default output is replaced with the raw output string. If a custom format is chosen, the `pairwise_choice` and `explanation` fields in the corresponding metric result will be empty.
              &quot;returnRawOutput&quot;: True or False, # Optional. Whether to return raw output.
            },
            &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Metric prompt template for pairwise metric.
            &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for pairwise metric.
          },
          &quot;pointwiseMetricSpec&quot;: { # Spec for pointwise metric. # Spec for pointwise metric.
            &quot;customOutputFormatConfig&quot;: { # Spec for custom output format configuration. # Optional. CustomOutputFormatConfig allows customization of metric output. By default, metrics return a score and explanation. When this config is set, the default output is replaced with either: - The raw output string. - A parsed output based on a user-defined schema. If a custom format is chosen, the `score` and `explanation` fields in the corresponding metric result will be empty.
              &quot;returnRawOutput&quot;: True or False, # Optional. Whether to return raw output.
            },
            &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Metric prompt template for pointwise metric.
            &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for pointwise metric.
          },
          &quot;predefinedMetricSpec&quot;: { # The spec for a pre-defined metric. # The spec for a pre-defined metric.
            &quot;metricSpecName&quot;: &quot;A String&quot;, # Required. The name of a pre-defined metric, such as &quot;instruction_following_v1&quot; or &quot;text_quality_v1&quot;.
            &quot;metricSpecParameters&quot;: { # Optional. The parameters needed to run the pre-defined metric.
              &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
            },
          },
          &quot;rougeSpec&quot;: { # Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1. # Spec for rouge metric.
            &quot;rougeType&quot;: &quot;A String&quot;, # Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.
            &quot;splitSummaries&quot;: True or False, # Optional. Whether to split summaries while using rougeLsum.
            &quot;useStemmer&quot;: True or False, # Optional. Whether to use stemmer to compute rouge score.
          },
        },
      ],
      &quot;outputConfig&quot;: { # Config for evaluation output. # Required. Config for evaluation output.
        &quot;gcsDestination&quot;: { # The Google Cloud Storage location where the output is to be written to. # Cloud storage destination for evaluation output.
          &quot;outputUriPrefix&quot;: &quot;A String&quot;, # Required. Google Cloud Storage URI to output directory. If the uri doesn&#x27;t end with &#x27;/&#x27;, a &#x27;/&#x27; will be automatically appended. The directory is created if it doesn&#x27;t exist.
        },
      },
    },
    &quot;exportLastCheckpointOnly&quot;: True or False, # Optional. If set to true, disable intermediate checkpoints for SFT and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for SFT. Default is false.
    &quot;hyperParameters&quot;: { # Hyperparameters for SFT. # Optional. Hyperparameters for SFT.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for tuning.
      &quot;batchSize&quot;: &quot;A String&quot;, # Optional. Batch size for tuning. This feature is only available for open source models.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRate&quot;: 3.14, # Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    &quot;tuningMode&quot;: &quot;A String&quot;, # Tuning mode.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
  },
  &quot;tunedModel&quot;: { # The Model Registry Model and Online Prediction Endpoint associated with this TuningJob. # Output only. The tuned model resources associated with this TuningJob.
    &quot;checkpoints&quot;: [ # Output only. The checkpoints associated with this TunedModel. This field is only populated for tuning jobs that enable intermediate checkpoints.
      { # TunedModelCheckpoint for the Tuned Model of a Tuning Job.
        &quot;checkpointId&quot;: &quot;A String&quot;, # The ID of the checkpoint.
        &quot;endpoint&quot;: &quot;A String&quot;, # The Endpoint resource name that the checkpoint is deployed to. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.
        &quot;epoch&quot;: &quot;A String&quot;, # The epoch of the checkpoint.
        &quot;step&quot;: &quot;A String&quot;, # The step of the checkpoint.
      },
    ],
    &quot;endpoint&quot;: &quot;A String&quot;, # Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.
    &quot;model&quot;: &quot;A String&quot;, # Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}@{version_id}` When tuning from a base model, the version ID will be 1. For continuous tuning, if the provided tuned_model_display_name is set and different from parent model&#x27;s display name, the tuned model will have a new parent model with version 1. Otherwise the version id will be incremented by 1 from the last version ID in the parent model. E.g., `projects/{project}/locations/{location}/models/{model}@{last_version_id + 1}`
  },
  &quot;tunedModelDisplayName&quot;: &quot;A String&quot;, # Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters. For continuous tuning, tuned_model_display_name will by default use the same display name as the pre-tuned model. If a new display name is provided, the tuning job will create a new model instead of a new version.
  &quot;tuningDataStats&quot;: { # The tuning data statistic values for TuningJob. # Output only. The tuning data statistics associated with this TuningJob.
    &quot;distillationDataStats&quot;: { # Statistics computed for datasets used for distillation. # Output only. Statistics for distillation.
      &quot;trainingDatasetStats&quot;: { # Statistics computed over a tuning dataset. # Output only. Statistics computed for the training dataset.
        &quot;droppedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
          &quot;A String&quot;,
        ],
        &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `dropped_example_indices`, the user-facing reason why the example was dropped.
          &quot;A String&quot;,
        ],
        &quot;totalBillableCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of billable characters in the tuning dataset.
        &quot;totalTuningCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of tuning characters in the tuning dataset.
        &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
        &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
        &quot;userDatasetExamples&quot;: [ # Output only. Sample user messages in the training dataset uri.
          { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
            &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
              { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                  &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                  &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                },
                &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                  &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                  &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                },
                &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                  &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                  &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                  &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                },
                &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                  &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                    &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                  },
                  &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                  &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                },
                &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                  &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                  &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                  &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                    { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                      &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                        &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                      &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                        &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                    },
                  ],
                  &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                    &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                  },
                },
                &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                  &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                  &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                  &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                },
                &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                  &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                  &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                  &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                },
              },
            ],
            &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
          },
        ],
        &quot;userInputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user input tokens.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
        &quot;userMessagePerExampleDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the messages per example.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
        &quot;userOutputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user output tokens.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
      },
    },
    &quot;preferenceOptimizationDataStats&quot;: { # Statistics computed for datasets used for preference optimization. # Output only. Statistics for preference optimization.
      &quot;droppedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
        &quot;A String&quot;,
      ],
      &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `dropped_example_indices`, the user-facing reason why the example was dropped.
        &quot;A String&quot;,
      ],
      &quot;scoreVariancePerExampleDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for scores variance per example.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
      &quot;scoresDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for scores.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
      &quot;totalBillableTokenCount&quot;: &quot;A String&quot;, # Output only. Number of billable tokens in the tuning dataset.
      &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
      &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
      &quot;userDatasetExamples&quot;: [ # Output only. Sample user examples in the training dataset.
        { # Input example for preference optimization.
          &quot;completions&quot;: [ # List of completions for a given prompt.
            { # Completion and its preference score.
              &quot;completion&quot;: { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message. # Single turn completion for the given prompt.
                &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                  { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                    &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                      &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                      &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                    },
                    &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                      &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                      &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                    },
                    &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                      &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                      &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                        &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                      },
                      &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                      &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                    },
                    &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                      &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                      &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                      &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                        { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                          &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                            &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                            &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                            &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                          },
                          &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                            &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                            &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                            &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                          },
                        },
                      ],
                      &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                        &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                      },
                    },
                    &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                      &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                    &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                    &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                    &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                      &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                      &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                      &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                    },
                  },
                ],
                &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
              },
              &quot;score&quot;: 3.14, # The score for the given completion.
            },
          ],
          &quot;contents&quot;: [ # Multi-turn contents that represents the Prompt.
            { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
              &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                  &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                    &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                    &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                  },
                  &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                    &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                    &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                  },
                  &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                    &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                    &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                  },
                  &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                    &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                      &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                    },
                    &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                    &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                  },
                  &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                    &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                    &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                    &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                      { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                        &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                          &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                        &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                          &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                      },
                    ],
                    &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                      &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                    },
                  },
                  &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                    &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                    &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                  },
                  &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                  &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                  &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                  &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                    &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                    &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                    &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                  },
                },
              ],
              &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
            },
          ],
        },
      ],
      &quot;userInputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user input tokens.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
      &quot;userOutputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user output tokens.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
    },
    &quot;supervisedTuningDataStats&quot;: { # Tuning data statistics for Supervised Tuning. # The SFT Tuning data stats.
      &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `truncated_example_indices`, the user-facing reason why the example was dropped.
        &quot;A String&quot;,
      ],
      &quot;totalBillableCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of billable characters in the tuning dataset.
      &quot;totalBillableTokenCount&quot;: &quot;A String&quot;, # Output only. Number of billable tokens in the tuning dataset.
      &quot;totalTruncatedExampleCount&quot;: &quot;A String&quot;, # Output only. The number of examples in the dataset that have been dropped. An example can be dropped for reasons including: too many tokens, contains an invalid image, contains too many images, etc.
      &quot;totalTuningCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of tuning characters in the tuning dataset.
      &quot;truncatedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
        &quot;A String&quot;,
      ],
      &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
      &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
      &quot;userDatasetExamples&quot;: [ # Output only. Sample user messages in the training dataset uri.
        { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
          &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
            { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
              &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
              },
              &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
              },
              &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
              },
              &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                  &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                },
                &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
              },
              &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                  { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                    &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                      &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                      &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                  },
                ],
                &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                  &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                },
              },
              &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
              },
              &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
              &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
              &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
              &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
              },
            },
          ],
          &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
        },
      ],
      &quot;userInputTokenDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user input tokens.
        &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
      },
      &quot;userMessagePerExampleDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the messages per example.
        &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
      },
      &quot;userOutputTokenDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user output tokens.
        &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
      },
    },
  },
  &quot;tuningJobState&quot;: &quot;A String&quot;, # Output only. The detail state of the tuning job (while the overall `JobState` is running).
  &quot;updateTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob was most recently updated.
  &quot;veoTuningSpec&quot;: { # Tuning Spec for Veo Model Tuning. # Tuning Spec for Veo Tuning.
    &quot;hyperParameters&quot;: { # Hyperparameters for Veo. # Optional. Hyperparameters for Veo.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
      &quot;tuningTask&quot;: &quot;A String&quot;, # Optional. The tuning task. Either I2V or T2V.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
  },
}</pre>
</div>

<div class="method">
    <code class="details" id="get">get(name, x__xgafv=None)</code>
  <pre>Gets a TuningJob.

Args:
  name: string, Required. The name of the TuningJob resource. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}` (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Represents a TuningJob that runs with Google owned models.
  &quot;baseModel&quot;: &quot;A String&quot;, # The base model that is being tuned. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).
  &quot;createTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob was created.
  &quot;customBaseModel&quot;: &quot;A String&quot;, # Optional. The user-provided path to custom model weights. Set this field to tune a custom model. The path must be a Cloud Storage directory that contains the model weights in .safetensors format along with associated model metadata files. If this field is set, the base_model field must still be set to indicate which base model the custom model is derived from. This feature is only available for open source models.
  &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the TuningJob.
  &quot;distillationSpec&quot;: { # Tuning Spec for Distillation. # Tuning Spec for Distillation.
    &quot;baseTeacherModel&quot;: &quot;A String&quot;, # The base teacher model that is being distilled. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).
    &quot;hyperParameters&quot;: { # Hyperparameters for Distillation. # Optional. Hyperparameters for Distillation.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for distillation.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
    },
    &quot;pipelineRootDirectory&quot;: &quot;A String&quot;, # Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.
    &quot;studentModel&quot;: &quot;A String&quot;, # The student model that is being tuned, e.g., &quot;google/gemma-2b-1.1-it&quot;. Deprecated. Use base_model instead.
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
    &quot;tunedTeacherModelSource&quot;: &quot;A String&quot;, # The resource name of the Tuned teacher model. Format: `projects/{project}/locations/{location}/models/{model}`.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.
  },
  &quot;encryptionSpec&quot;: { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key.
    &quot;kmsKeyName&quot;: &quot;A String&quot;, # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
  },
  &quot;endTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob entered any of the following JobStates: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`, `JOB_STATE_EXPIRED`.
  &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. Only populated when job&#x27;s state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  &quot;evaluateDatasetRuns&quot;: [ # Output only. Evaluation runs for the Tuning Job.
    { # Evaluate Dataset Run Result for Tuning Job.
      &quot;checkpointId&quot;: &quot;A String&quot;, # Output only. The checkpoint id used in the evaluation run. Only populated when evaluating checkpoints.
      &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. The error of the evaluation run if any.
        &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
        &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
          },
        ],
        &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
      },
      &quot;evaluateDatasetResponse&quot;: { # Response in LRO for EvaluationService.EvaluateDataset. # Output only. Results for EvaluationService.EvaluateDataset.
        &quot;aggregationOutput&quot;: { # The aggregation result for the entire dataset and all metrics. # Output only. Aggregation statistics derived from results of EvaluationService.EvaluateDataset.
          &quot;aggregationResults&quot;: [ # One AggregationResult per metric.
            { # The aggregation result for a single metric.
              &quot;aggregationMetric&quot;: &quot;A String&quot;, # Aggregation metric.
              &quot;bleuMetricValue&quot;: { # Bleu metric value for an instance. # Results for bleu metric.
                &quot;score&quot;: 3.14, # Output only. Bleu score.
              },
              &quot;customCodeExecutionResult&quot;: { # Result for custom code execution metric. # Result for code execution metric.
                &quot;score&quot;: 3.14, # Output only. Custom code execution score.
              },
              &quot;exactMatchMetricValue&quot;: { # Exact match metric value for an instance. # Results for exact match metric.
                &quot;score&quot;: 3.14, # Output only. Exact match score.
              },
              &quot;pairwiseMetricResult&quot;: { # Spec for pairwise metric result. # Result for pairwise metric.
                &quot;customOutput&quot;: { # Spec for custom output. # Output only. Spec for custom output.
                  &quot;rawOutputs&quot;: { # Raw output. # Output only. List of raw output strings.
                    &quot;rawOutput&quot;: [ # Output only. Raw output string.
                      &quot;A String&quot;,
                    ],
                  },
                },
                &quot;explanation&quot;: &quot;A String&quot;, # Output only. Explanation for pairwise metric score.
                &quot;pairwiseChoice&quot;: &quot;A String&quot;, # Output only. Pairwise metric choice.
              },
              &quot;pointwiseMetricResult&quot;: { # Spec for pointwise metric result. # Result for pointwise metric.
                &quot;customOutput&quot;: { # Spec for custom output. # Output only. Spec for custom output.
                  &quot;rawOutputs&quot;: { # Raw output. # Output only. List of raw output strings.
                    &quot;rawOutput&quot;: [ # Output only. Raw output string.
                      &quot;A String&quot;,
                    ],
                  },
                },
                &quot;explanation&quot;: &quot;A String&quot;, # Output only. Explanation for pointwise metric score.
                &quot;score&quot;: 3.14, # Output only. Pointwise metric score.
              },
              &quot;rougeMetricValue&quot;: { # Rouge metric value for an instance. # Results for rouge metric.
                &quot;score&quot;: 3.14, # Output only. Rouge score.
              },
            },
          ],
          &quot;dataset&quot;: { # The dataset used for evaluation. # The dataset used for evaluation &amp; aggregation.
            &quot;bigquerySource&quot;: { # The BigQuery location for the input content. # BigQuery source holds the dataset.
              &quot;inputUri&quot;: &quot;A String&quot;, # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
            },
            &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # Cloud storage source holds the dataset. Currently only one Cloud Storage file path is supported.
              &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
                &quot;A String&quot;,
              ],
            },
          },
        },
        &quot;outputInfo&quot;: { # Describes the info for output of EvaluationService.EvaluateDataset. # Output only. Output info for EvaluationService.EvaluateDataset.
          &quot;gcsOutputDirectory&quot;: &quot;A String&quot;, # Output only. The full path of the Cloud Storage directory created, into which the evaluation results and aggregation results are written.
        },
      },
      &quot;operationName&quot;: &quot;A String&quot;, # Output only. The operation ID of the evaluation run. Format: `projects/{project}/locations/{location}/operations/{operation_id}`.
    },
  ],
  &quot;experiment&quot;: &quot;A String&quot;, # Output only. The Experiment associated with this TuningJob.
  &quot;fullFineTuningSpec&quot;: { # Tuning Spec for Full Fine Tuning. # Tuning Spec for Full Fine Tuning.
    &quot;hyperParameters&quot;: { # Hyperparameters for SFT. # Optional. Hyperparameters for Full Fine Tuning.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for tuning.
      &quot;batchSize&quot;: &quot;A String&quot;, # Optional. Batch size for tuning. This feature is only available for open source models.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRate&quot;: 3.14, # Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
  },
  &quot;labels&quot;: { # Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
    &quot;a_key&quot;: &quot;A String&quot;,
  },
  &quot;name&quot;: &quot;A String&quot;, # Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`
  &quot;outputUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to the directory where tuning job outputs are written to. This field is only available and required for open source models.
  &quot;partnerModelTuningSpec&quot;: { # Tuning spec for Partner models. # Tuning Spec for open sourced and third party Partner models.
    &quot;hyperParameters&quot;: { # Hyperparameters for tuning. The accepted hyper_parameters and their valid range of values will differ depending on the base model.
      &quot;a_key&quot;: &quot;&quot;,
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.
  },
  &quot;pipelineJob&quot;: &quot;A String&quot;, # Output only. The resource name of the PipelineJob associated with the TuningJob. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}`.
  &quot;preTunedModel&quot;: { # A pre-tuned model for continuous tuning. # The pre-tuned model for continuous tuning.
    &quot;baseModel&quot;: &quot;A String&quot;, # Output only. The name of the base model this PreTunedModel was tuned from.
    &quot;checkpointId&quot;: &quot;A String&quot;, # Optional. The source checkpoint id. If not specified, the default checkpoint will be used.
    &quot;tunedModelName&quot;: &quot;A String&quot;, # The resource name of the Model. E.g., a model resource name with a specified version id or alias: `projects/{project}/locations/{location}/models/{model}@{version_id}` `projects/{project}/locations/{location}/models/{model}@{alias}` Or, omit the version id to use the default version: `projects/{project}/locations/{location}/models/{model}`
  },
  &quot;preferenceOptimizationSpec&quot;: { # Tuning Spec for Preference Optimization. # Tuning Spec for Preference Optimization.
    &quot;exportLastCheckpointOnly&quot;: True or False, # Optional. If set to true, disable intermediate checkpoints for Preference Optimization and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for Preference Optimization. Default is false.
    &quot;hyperParameters&quot;: { # Hyperparameters for Preference Optimization. # Optional. Hyperparameters for Preference Optimization.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for preference optimization.
      &quot;beta&quot;: 3.14, # Optional. Weight for KL Divergence regularization.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.
  },
  &quot;satisfiesPzi&quot;: True or False, # Output only. Reserved for future use.
  &quot;satisfiesPzs&quot;: True or False, # Output only. Reserved for future use.
  &quot;serviceAccount&quot;: &quot;A String&quot;, # The service account that the tuningJob workload runs as. If not specified, the Vertex AI Secure Fine-Tuned Service Agent in the project will be used. See https://cloud.google.com/iam/docs/service-agents#vertex-ai-secure-fine-tuning-service-agent Users starting the pipeline must have the `iam.serviceAccounts.actAs` permission on this service account.
  &quot;startTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state.
  &quot;state&quot;: &quot;A String&quot;, # Output only. The detailed state of the job.
  &quot;supervisedTuningSpec&quot;: { # Tuning Spec for Supervised Tuning for first party models. # Tuning Spec for Supervised Fine Tuning.
    &quot;evaluationConfig&quot;: { # Evaluation Config for Tuning Job. # Optional. Evaluation Config for Tuning Job.
      &quot;autoraterConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config for evaluation.
        &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
        &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
        &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
          &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
          &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
          &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
          &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
          &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
            &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
            &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
              &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
              &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
            },
            &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
          },
          &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
          &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
          &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
          &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
            &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
          },
          &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
          &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
          &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
          &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
          &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
            &quot;A String&quot;,
          ],
          &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
            &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
            &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
              # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            ],
            &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
            &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
              &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            },
            &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
            &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
              &quot;A String&quot;,
            ],
            &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
            &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
            &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
            &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
            &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
            &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
            &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
            &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
            &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
            &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
            &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
            &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
            &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
            &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
              &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            },
            &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
              &quot;A String&quot;,
            ],
            &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
            &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
              &quot;A String&quot;,
            ],
            &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
            &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
          },
          &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
            &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
              &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
            },
            &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
              &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
            },
          },
          &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
          &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
            &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
            &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
              &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                { # Configuration for a single speaker in a multi-speaker setup.
                  &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                  &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                    &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                      &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                    },
                  },
                },
              ],
            },
            &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
              &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
              },
            },
          },
          &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
            &quot;A String&quot;,
          ],
          &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
          &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
            &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
            &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
          },
          &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
          &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
        },
        &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
      },
      &quot;metrics&quot;: [ # Required. The metrics used for evaluation.
        { # The metric used for running evaluations.
          &quot;aggregationMetrics&quot;: [ # Optional. The aggregation metrics to use.
            &quot;A String&quot;,
          ],
          &quot;bleuSpec&quot;: { # Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1. # Spec for bleu metric.
            &quot;useEffectiveOrder&quot;: True or False, # Optional. Whether to use_effective_order to compute bleu score.
          },
          &quot;customCodeExecutionSpec&quot;: { # Specificies a metric that is populated by evaluating user-defined Python code. # Spec for Custom Code Execution metric.
            &quot;evaluationFunction&quot;: &quot;A String&quot;, # Required. Python function. Expected user to define the following function, e.g.: def evaluate(instance: dict[str, Any]) -&gt; float: Please include this function signature in the code snippet. Instance is the evaluation instance, any fields populated in the instance are available to the function as instance[field_name]. Example: Example input: ``` instance= EvaluationInstance( response=EvaluationInstance.InstanceData(text=&quot;The answer is 4.&quot;), reference=EvaluationInstance.InstanceData(text=&quot;4&quot;) ) ``` Example converted input: ``` { &#x27;response&#x27;: {&#x27;text&#x27;: &#x27;The answer is 4.&#x27;}, &#x27;reference&#x27;: {&#x27;text&#x27;: &#x27;4&#x27;} } ``` Example python function: ``` def evaluate(instance: dict[str, Any]) -&gt; float: if instance&#x27;response&#x27; == instance&#x27;reference&#x27;: return 1.0 return 0.0 ```
          },
          &quot;exactMatchSpec&quot;: { # Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0. # Spec for exact match metric.
          },
          &quot;llmBasedMetricSpec&quot;: { # Specification for an LLM based metric. # Spec for an LLM based metric.
            &quot;additionalConfig&quot;: { # Optional. Optional additional configuration for the metric.
              &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
            },
            &quot;judgeAutoraterConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Optional configuration for the judge LLM (Autorater).
              &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
              &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
              &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
                &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
                &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
                &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
                &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
                &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                  &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                  &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                    &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                  },
                  &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
                },
                &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
                &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
                &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
                &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                  &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
                },
                &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
                &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
                &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
                &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
                &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                  &quot;A String&quot;,
                ],
                &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                  &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                  &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                    # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  ],
                  &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                  &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                    &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                  &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                    &quot;A String&quot;,
                  ],
                  &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                  &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                  &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                  &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                  &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                  &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                  &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                  &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                  &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                  &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                  &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                  &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                  &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                  &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                    &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                    &quot;A String&quot;,
                  ],
                  &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                  &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                    &quot;A String&quot;,
                  ],
                  &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                  &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
                },
                &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                  &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                    &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                  },
                  &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                    &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                  },
                },
                &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
                &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                  &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                  &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                    &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                      { # Configuration for a single speaker in a multi-speaker setup.
                        &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                        &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                          &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                            &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                          },
                        },
                      },
                    ],
                  },
                  &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                    &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                      &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                    },
                  },
                },
                &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                  &quot;A String&quot;,
                ],
                &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
                &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                  &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                  &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
                },
                &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
                &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
              },
              &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
            },
            &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Template for the prompt sent to the judge model.
            &quot;predefinedRubricGenerationSpec&quot;: { # The spec for a pre-defined metric. # Dynamically generate rubrics using a predefined spec.
              &quot;metricSpecName&quot;: &quot;A String&quot;, # Required. The name of a pre-defined metric, such as &quot;instruction_following_v1&quot; or &quot;text_quality_v1&quot;.
              &quot;metricSpecParameters&quot;: { # Optional. The parameters needed to run the pre-defined metric.
                &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
              },
            },
            &quot;rubricGenerationSpec&quot;: { # Specification for how rubrics should be generated. # Dynamically generate rubrics using this specification.
              &quot;modelConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Configuration for the model used in rubric generation. Configs including sampling count and base model can be specified here. Flipping is not supported for rubric generation.
                &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
                &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
                &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
                  &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
                  &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
                  &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
                  &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
                  &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                    &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                    &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                      &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                    },
                    &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
                  },
                  &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
                  &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
                  &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
                  &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                    &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
                  },
                  &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
                  &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
                  &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
                  &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
                  &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                    &quot;A String&quot;,
                  ],
                  &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                    &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                    &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                      # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    ],
                    &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                    &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                      &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    },
                    &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                    &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                      &quot;A String&quot;,
                    ],
                    &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                    &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                    &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                    &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                    &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                    &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                    &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                    &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                    &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                    &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                    &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                    &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                    &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                    &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                      &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    },
                    &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                      &quot;A String&quot;,
                    ],
                    &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                    &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                      &quot;A String&quot;,
                    ],
                    &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                    &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
                  },
                  &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                    &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                      &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                    },
                    &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                      &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                    },
                  },
                  &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
                  &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                    &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                    &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                      &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                        { # Configuration for a single speaker in a multi-speaker setup.
                          &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                          &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                            &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                              &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                            },
                          },
                        },
                      ],
                    },
                    &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                      &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                        &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                      },
                    },
                  },
                  &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                    &quot;A String&quot;,
                  ],
                  &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
                  &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                    &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                    &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
                  },
                  &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
                  &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
                },
                &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
              },
              &quot;promptTemplate&quot;: &quot;A String&quot;, # Template for the prompt used to generate rubrics. The details should be updated based on the most-recent recipe requirements.
              &quot;rubricContentType&quot;: &quot;A String&quot;, # The type of rubric content to be generated.
              &quot;rubricTypeOntology&quot;: [ # Optional. An optional, pre-defined list of allowed types for generated rubrics. If this field is provided, it implies `include_rubric_type` should be true, and the generated rubric types should be chosen from this ontology.
                &quot;A String&quot;,
              ],
            },
            &quot;rubricGroupKey&quot;: &quot;A String&quot;, # Use a pre-defined group of rubrics associated with the input. Refers to a key in the rubric_groups map of EvaluationInstance.
            &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for the judge model.
          },
          &quot;pairwiseMetricSpec&quot;: { # Spec for pairwise metric. # Spec for pairwise metric.
            &quot;baselineResponseFieldName&quot;: &quot;A String&quot;, # Optional. The field name of the baseline response.
            &quot;candidateResponseFieldName&quot;: &quot;A String&quot;, # Optional. The field name of the candidate response.
            &quot;customOutputFormatConfig&quot;: { # Spec for custom output format configuration. # Optional. CustomOutputFormatConfig allows customization of metric output. When this config is set, the default output is replaced with the raw output string. If a custom format is chosen, the `pairwise_choice` and `explanation` fields in the corresponding metric result will be empty.
              &quot;returnRawOutput&quot;: True or False, # Optional. Whether to return raw output.
            },
            &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Metric prompt template for pairwise metric.
            &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for pairwise metric.
          },
          &quot;pointwiseMetricSpec&quot;: { # Spec for pointwise metric. # Spec for pointwise metric.
            &quot;customOutputFormatConfig&quot;: { # Spec for custom output format configuration. # Optional. CustomOutputFormatConfig allows customization of metric output. By default, metrics return a score and explanation. When this config is set, the default output is replaced with either: - The raw output string. - A parsed output based on a user-defined schema. If a custom format is chosen, the `score` and `explanation` fields in the corresponding metric result will be empty.
              &quot;returnRawOutput&quot;: True or False, # Optional. Whether to return raw output.
            },
            &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Metric prompt template for pointwise metric.
            &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for pointwise metric.
          },
          &quot;predefinedMetricSpec&quot;: { # The spec for a pre-defined metric. # The spec for a pre-defined metric.
            &quot;metricSpecName&quot;: &quot;A String&quot;, # Required. The name of a pre-defined metric, such as &quot;instruction_following_v1&quot; or &quot;text_quality_v1&quot;.
            &quot;metricSpecParameters&quot;: { # Optional. The parameters needed to run the pre-defined metric.
              &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
            },
          },
          &quot;rougeSpec&quot;: { # Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1. # Spec for rouge metric.
            &quot;rougeType&quot;: &quot;A String&quot;, # Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.
            &quot;splitSummaries&quot;: True or False, # Optional. Whether to split summaries while using rougeLsum.
            &quot;useStemmer&quot;: True or False, # Optional. Whether to use stemmer to compute rouge score.
          },
        },
      ],
      &quot;outputConfig&quot;: { # Config for evaluation output. # Required. Config for evaluation output.
        &quot;gcsDestination&quot;: { # The Google Cloud Storage location where the output is to be written to. # Cloud storage destination for evaluation output.
          &quot;outputUriPrefix&quot;: &quot;A String&quot;, # Required. Google Cloud Storage URI to output directory. If the uri doesn&#x27;t end with &#x27;/&#x27;, a &#x27;/&#x27; will be automatically appended. The directory is created if it doesn&#x27;t exist.
        },
      },
    },
    &quot;exportLastCheckpointOnly&quot;: True or False, # Optional. If set to true, disable intermediate checkpoints for SFT and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for SFT. Default is false.
    &quot;hyperParameters&quot;: { # Hyperparameters for SFT. # Optional. Hyperparameters for SFT.
      &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for tuning.
      &quot;batchSize&quot;: &quot;A String&quot;, # Optional. Batch size for tuning. This feature is only available for open source models.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRate&quot;: 3.14, # Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    &quot;tuningMode&quot;: &quot;A String&quot;, # Tuning mode.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
  },
  &quot;tunedModel&quot;: { # The Model Registry Model and Online Prediction Endpoint associated with this TuningJob. # Output only. The tuned model resources associated with this TuningJob.
    &quot;checkpoints&quot;: [ # Output only. The checkpoints associated with this TunedModel. This field is only populated for tuning jobs that enable intermediate checkpoints.
      { # TunedModelCheckpoint for the Tuned Model of a Tuning Job.
        &quot;checkpointId&quot;: &quot;A String&quot;, # The ID of the checkpoint.
        &quot;endpoint&quot;: &quot;A String&quot;, # The Endpoint resource name that the checkpoint is deployed to. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.
        &quot;epoch&quot;: &quot;A String&quot;, # The epoch of the checkpoint.
        &quot;step&quot;: &quot;A String&quot;, # The step of the checkpoint.
      },
    ],
    &quot;endpoint&quot;: &quot;A String&quot;, # Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.
    &quot;model&quot;: &quot;A String&quot;, # Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}@{version_id}` When tuning from a base model, the version ID will be 1. For continuous tuning, if the provided tuned_model_display_name is set and different from parent model&#x27;s display name, the tuned model will have a new parent model with version 1. Otherwise the version id will be incremented by 1 from the last version ID in the parent model. E.g., `projects/{project}/locations/{location}/models/{model}@{last_version_id + 1}`
  },
  &quot;tunedModelDisplayName&quot;: &quot;A String&quot;, # Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters. For continuous tuning, tuned_model_display_name will by default use the same display name as the pre-tuned model. If a new display name is provided, the tuning job will create a new model instead of a new version.
  &quot;tuningDataStats&quot;: { # The tuning data statistic values for TuningJob. # Output only. The tuning data statistics associated with this TuningJob.
    &quot;distillationDataStats&quot;: { # Statistics computed for datasets used for distillation. # Output only. Statistics for distillation.
      &quot;trainingDatasetStats&quot;: { # Statistics computed over a tuning dataset. # Output only. Statistics computed for the training dataset.
        &quot;droppedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
          &quot;A String&quot;,
        ],
        &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `dropped_example_indices`, the user-facing reason why the example was dropped.
          &quot;A String&quot;,
        ],
        &quot;totalBillableCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of billable characters in the tuning dataset.
        &quot;totalTuningCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of tuning characters in the tuning dataset.
        &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
        &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
        &quot;userDatasetExamples&quot;: [ # Output only. Sample user messages in the training dataset uri.
          { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
            &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
              { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                  &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                  &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                },
                &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                  &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                  &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                },
                &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                  &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                  &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                  &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                },
                &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                  &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                    &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                  },
                  &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                  &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                },
                &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                  &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                  &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                  &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                    { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                      &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                        &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                      &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                        &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                    },
                  ],
                  &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                    &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                  },
                },
                &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                  &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                  &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                  &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                },
                &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                  &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                  &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                  &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                },
              },
            ],
            &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
          },
        ],
        &quot;userInputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user input tokens.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
        &quot;userMessagePerExampleDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the messages per example.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
        &quot;userOutputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user output tokens.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
      },
    },
    &quot;preferenceOptimizationDataStats&quot;: { # Statistics computed for datasets used for preference optimization. # Output only. Statistics for preference optimization.
      &quot;droppedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
        &quot;A String&quot;,
      ],
      &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `dropped_example_indices`, the user-facing reason why the example was dropped.
        &quot;A String&quot;,
      ],
      &quot;scoreVariancePerExampleDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for scores variance per example.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
      &quot;scoresDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for scores.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
      &quot;totalBillableTokenCount&quot;: &quot;A String&quot;, # Output only. Number of billable tokens in the tuning dataset.
      &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
      &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
      &quot;userDatasetExamples&quot;: [ # Output only. Sample user examples in the training dataset.
        { # Input example for preference optimization.
          &quot;completions&quot;: [ # List of completions for a given prompt.
            { # Completion and its preference score.
              &quot;completion&quot;: { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message. # Single turn completion for the given prompt.
                &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                  { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                    &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                      &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                      &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                    },
                    &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                      &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                      &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                    },
                    &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                      &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                      &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                        &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                      },
                      &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                      &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                    },
                    &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                      &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                      &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                      &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                        { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                          &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                            &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                            &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                            &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                          },
                          &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                            &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                            &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                            &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                          },
                        },
                      ],
                      &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                        &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                      },
                    },
                    &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                      &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                    &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                    &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                    &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                      &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                      &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                      &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                    },
                  },
                ],
                &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
              },
              &quot;score&quot;: 3.14, # The score for the given completion.
            },
          ],
          &quot;contents&quot;: [ # Multi-turn contents that represents the Prompt.
            { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
              &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                  &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                    &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                    &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                  },
                  &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                    &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                    &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                  },
                  &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                    &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                    &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                  },
                  &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                    &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                      &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                    },
                    &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                    &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                  },
                  &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                    &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                    &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                    &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                      { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                        &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                          &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                        &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                          &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                      },
                    ],
                    &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                      &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                    },
                  },
                  &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                    &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                    &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                  },
                  &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                  &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                  &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                  &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                    &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                    &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                    &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                  },
                },
              ],
              &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
            },
          ],
        },
      ],
      &quot;userInputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user input tokens.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
      &quot;userOutputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user output tokens.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
      },
    },
    &quot;supervisedTuningDataStats&quot;: { # Tuning data statistics for Supervised Tuning. # The SFT Tuning data stats.
      &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `truncated_example_indices`, the user-facing reason why the example was dropped.
        &quot;A String&quot;,
      ],
      &quot;totalBillableCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of billable characters in the tuning dataset.
      &quot;totalBillableTokenCount&quot;: &quot;A String&quot;, # Output only. Number of billable tokens in the tuning dataset.
      &quot;totalTruncatedExampleCount&quot;: &quot;A String&quot;, # Output only. The number of examples in the dataset that have been dropped. An example can be dropped for reasons including: too many tokens, contains an invalid image, contains too many images, etc.
      &quot;totalTuningCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of tuning characters in the tuning dataset.
      &quot;truncatedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
        &quot;A String&quot;,
      ],
      &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
      &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
      &quot;userDatasetExamples&quot;: [ # Output only. Sample user messages in the training dataset uri.
        { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
          &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
            { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
              &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
              },
              &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
              },
              &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
              },
              &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                  &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                },
                &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
              },
              &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                  { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                    &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                      &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                      &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                  },
                ],
                &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                  &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                },
              },
              &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
              },
              &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
              &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
              &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
              &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
              },
            },
          ],
          &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
        },
      ],
      &quot;userInputTokenDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user input tokens.
        &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
      },
      &quot;userMessagePerExampleDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the messages per example.
        &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
      },
      &quot;userOutputTokenDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user output tokens.
        &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
        &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
          { # Dataset bucket used to create a histogram for the distribution given a population of values.
            &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
            &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
            &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
          },
        ],
        &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
        &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
        &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
        &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
        &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
        &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
        &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
      },
    },
  },
  &quot;tuningJobState&quot;: &quot;A String&quot;, # Output only. The detail state of the tuning job (while the overall `JobState` is running).
  &quot;updateTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob was most recently updated.
  &quot;veoTuningSpec&quot;: { # Tuning Spec for Veo Model Tuning. # Tuning Spec for Veo Tuning.
    &quot;hyperParameters&quot;: { # Hyperparameters for Veo. # Optional. Hyperparameters for Veo.
      &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
      &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
      &quot;tuningTask&quot;: &quot;A String&quot;, # Optional. The tuning task. Either I2V or T2V.
    },
    &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
  },
}</pre>
</div>

<div class="method">
    <code class="details" id="list">list(parent, filter=None, pageSize=None, pageToken=None, x__xgafv=None)</code>
  <pre>Lists TuningJobs in a Location.

Args:
  parent: string, Required. The resource name of the Location to list the TuningJobs from. Format: `projects/{project}/locations/{location}` (required)
  filter: string, Optional. The standard list filter.
  pageSize: integer, Optional. The standard list page size.
  pageToken: string, Optional. The standard list page token. Typically obtained via ListTuningJobsResponse.next_page_token of the previous GenAiTuningService.ListTuningJob][] call.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for GenAiTuningService.ListTuningJobs
  &quot;nextPageToken&quot;: &quot;A String&quot;, # A token to retrieve the next page of results. Pass to ListTuningJobsRequest.page_token to obtain that page.
  &quot;tuningJobs&quot;: [ # List of TuningJobs in the requested page.
    { # Represents a TuningJob that runs with Google owned models.
      &quot;baseModel&quot;: &quot;A String&quot;, # The base model that is being tuned. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).
      &quot;createTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob was created.
      &quot;customBaseModel&quot;: &quot;A String&quot;, # Optional. The user-provided path to custom model weights. Set this field to tune a custom model. The path must be a Cloud Storage directory that contains the model weights in .safetensors format along with associated model metadata files. If this field is set, the base_model field must still be set to indicate which base model the custom model is derived from. This feature is only available for open source models.
      &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the TuningJob.
      &quot;distillationSpec&quot;: { # Tuning Spec for Distillation. # Tuning Spec for Distillation.
        &quot;baseTeacherModel&quot;: &quot;A String&quot;, # The base teacher model that is being distilled. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).
        &quot;hyperParameters&quot;: { # Hyperparameters for Distillation. # Optional. Hyperparameters for Distillation.
          &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for distillation.
          &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
          &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
        },
        &quot;pipelineRootDirectory&quot;: &quot;A String&quot;, # Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.
        &quot;studentModel&quot;: &quot;A String&quot;, # The student model that is being tuned, e.g., &quot;google/gemma-2b-1.1-it&quot;. Deprecated. Use base_model instead.
        &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
        &quot;tunedTeacherModelSource&quot;: &quot;A String&quot;, # The resource name of the Tuned teacher model. Format: `projects/{project}/locations/{location}/models/{model}`.
        &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.
      },
      &quot;encryptionSpec&quot;: { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key.
        &quot;kmsKeyName&quot;: &quot;A String&quot;, # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
      },
      &quot;endTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob entered any of the following JobStates: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`, `JOB_STATE_EXPIRED`.
      &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. Only populated when job&#x27;s state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
        &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
        &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
          },
        ],
        &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
      },
      &quot;evaluateDatasetRuns&quot;: [ # Output only. Evaluation runs for the Tuning Job.
        { # Evaluate Dataset Run Result for Tuning Job.
          &quot;checkpointId&quot;: &quot;A String&quot;, # Output only. The checkpoint id used in the evaluation run. Only populated when evaluating checkpoints.
          &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. The error of the evaluation run if any.
            &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
            &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
              {
                &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
              },
            ],
            &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
          },
          &quot;evaluateDatasetResponse&quot;: { # Response in LRO for EvaluationService.EvaluateDataset. # Output only. Results for EvaluationService.EvaluateDataset.
            &quot;aggregationOutput&quot;: { # The aggregation result for the entire dataset and all metrics. # Output only. Aggregation statistics derived from results of EvaluationService.EvaluateDataset.
              &quot;aggregationResults&quot;: [ # One AggregationResult per metric.
                { # The aggregation result for a single metric.
                  &quot;aggregationMetric&quot;: &quot;A String&quot;, # Aggregation metric.
                  &quot;bleuMetricValue&quot;: { # Bleu metric value for an instance. # Results for bleu metric.
                    &quot;score&quot;: 3.14, # Output only. Bleu score.
                  },
                  &quot;customCodeExecutionResult&quot;: { # Result for custom code execution metric. # Result for code execution metric.
                    &quot;score&quot;: 3.14, # Output only. Custom code execution score.
                  },
                  &quot;exactMatchMetricValue&quot;: { # Exact match metric value for an instance. # Results for exact match metric.
                    &quot;score&quot;: 3.14, # Output only. Exact match score.
                  },
                  &quot;pairwiseMetricResult&quot;: { # Spec for pairwise metric result. # Result for pairwise metric.
                    &quot;customOutput&quot;: { # Spec for custom output. # Output only. Spec for custom output.
                      &quot;rawOutputs&quot;: { # Raw output. # Output only. List of raw output strings.
                        &quot;rawOutput&quot;: [ # Output only. Raw output string.
                          &quot;A String&quot;,
                        ],
                      },
                    },
                    &quot;explanation&quot;: &quot;A String&quot;, # Output only. Explanation for pairwise metric score.
                    &quot;pairwiseChoice&quot;: &quot;A String&quot;, # Output only. Pairwise metric choice.
                  },
                  &quot;pointwiseMetricResult&quot;: { # Spec for pointwise metric result. # Result for pointwise metric.
                    &quot;customOutput&quot;: { # Spec for custom output. # Output only. Spec for custom output.
                      &quot;rawOutputs&quot;: { # Raw output. # Output only. List of raw output strings.
                        &quot;rawOutput&quot;: [ # Output only. Raw output string.
                          &quot;A String&quot;,
                        ],
                      },
                    },
                    &quot;explanation&quot;: &quot;A String&quot;, # Output only. Explanation for pointwise metric score.
                    &quot;score&quot;: 3.14, # Output only. Pointwise metric score.
                  },
                  &quot;rougeMetricValue&quot;: { # Rouge metric value for an instance. # Results for rouge metric.
                    &quot;score&quot;: 3.14, # Output only. Rouge score.
                  },
                },
              ],
              &quot;dataset&quot;: { # The dataset used for evaluation. # The dataset used for evaluation &amp; aggregation.
                &quot;bigquerySource&quot;: { # The BigQuery location for the input content. # BigQuery source holds the dataset.
                  &quot;inputUri&quot;: &quot;A String&quot;, # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
                },
                &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # Cloud storage source holds the dataset. Currently only one Cloud Storage file path is supported.
                  &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
                    &quot;A String&quot;,
                  ],
                },
              },
            },
            &quot;outputInfo&quot;: { # Describes the info for output of EvaluationService.EvaluateDataset. # Output only. Output info for EvaluationService.EvaluateDataset.
              &quot;gcsOutputDirectory&quot;: &quot;A String&quot;, # Output only. The full path of the Cloud Storage directory created, into which the evaluation results and aggregation results are written.
            },
          },
          &quot;operationName&quot;: &quot;A String&quot;, # Output only. The operation ID of the evaluation run. Format: `projects/{project}/locations/{location}/operations/{operation_id}`.
        },
      ],
      &quot;experiment&quot;: &quot;A String&quot;, # Output only. The Experiment associated with this TuningJob.
      &quot;fullFineTuningSpec&quot;: { # Tuning Spec for Full Fine Tuning. # Tuning Spec for Full Fine Tuning.
        &quot;hyperParameters&quot;: { # Hyperparameters for SFT. # Optional. Hyperparameters for Full Fine Tuning.
          &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for tuning.
          &quot;batchSize&quot;: &quot;A String&quot;, # Optional. Batch size for tuning. This feature is only available for open source models.
          &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
          &quot;learningRate&quot;: 3.14, # Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.
          &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.
        },
        &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
        &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
      },
      &quot;labels&quot;: { # Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
        &quot;a_key&quot;: &quot;A String&quot;,
      },
      &quot;name&quot;: &quot;A String&quot;, # Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`
      &quot;outputUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to the directory where tuning job outputs are written to. This field is only available and required for open source models.
      &quot;partnerModelTuningSpec&quot;: { # Tuning spec for Partner models. # Tuning Spec for open sourced and third party Partner models.
        &quot;hyperParameters&quot;: { # Hyperparameters for tuning. The accepted hyper_parameters and their valid range of values will differ depending on the base model.
          &quot;a_key&quot;: &quot;&quot;,
        },
        &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
        &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.
      },
      &quot;pipelineJob&quot;: &quot;A String&quot;, # Output only. The resource name of the PipelineJob associated with the TuningJob. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}`.
      &quot;preTunedModel&quot;: { # A pre-tuned model for continuous tuning. # The pre-tuned model for continuous tuning.
        &quot;baseModel&quot;: &quot;A String&quot;, # Output only. The name of the base model this PreTunedModel was tuned from.
        &quot;checkpointId&quot;: &quot;A String&quot;, # Optional. The source checkpoint id. If not specified, the default checkpoint will be used.
        &quot;tunedModelName&quot;: &quot;A String&quot;, # The resource name of the Model. E.g., a model resource name with a specified version id or alias: `projects/{project}/locations/{location}/models/{model}@{version_id}` `projects/{project}/locations/{location}/models/{model}@{alias}` Or, omit the version id to use the default version: `projects/{project}/locations/{location}/models/{model}`
      },
      &quot;preferenceOptimizationSpec&quot;: { # Tuning Spec for Preference Optimization. # Tuning Spec for Preference Optimization.
        &quot;exportLastCheckpointOnly&quot;: True or False, # Optional. If set to true, disable intermediate checkpoints for Preference Optimization and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for Preference Optimization. Default is false.
        &quot;hyperParameters&quot;: { # Hyperparameters for Preference Optimization. # Optional. Hyperparameters for Preference Optimization.
          &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for preference optimization.
          &quot;beta&quot;: 3.14, # Optional. Weight for KL Divergence regularization.
          &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
          &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
        },
        &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.
        &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.
      },
      &quot;satisfiesPzi&quot;: True or False, # Output only. Reserved for future use.
      &quot;satisfiesPzs&quot;: True or False, # Output only. Reserved for future use.
      &quot;serviceAccount&quot;: &quot;A String&quot;, # The service account that the tuningJob workload runs as. If not specified, the Vertex AI Secure Fine-Tuned Service Agent in the project will be used. See https://cloud.google.com/iam/docs/service-agents#vertex-ai-secure-fine-tuning-service-agent Users starting the pipeline must have the `iam.serviceAccounts.actAs` permission on this service account.
      &quot;startTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state.
      &quot;state&quot;: &quot;A String&quot;, # Output only. The detailed state of the job.
      &quot;supervisedTuningSpec&quot;: { # Tuning Spec for Supervised Tuning for first party models. # Tuning Spec for Supervised Fine Tuning.
        &quot;evaluationConfig&quot;: { # Evaluation Config for Tuning Job. # Optional. Evaluation Config for Tuning Job.
          &quot;autoraterConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config for evaluation.
            &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
            &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
            &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
              &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
              &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
              &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
              &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
              &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                  &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                  &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                },
                &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
              },
              &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
              &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
              &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
              &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
              },
              &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
              &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
              &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
              &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
              &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                &quot;A String&quot;,
              ],
              &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                  # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                ],
                &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                  &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                },
                &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                  &quot;A String&quot;,
                ],
                &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                  &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                },
                &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                  &quot;A String&quot;,
                ],
                &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                  &quot;A String&quot;,
                ],
                &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
              },
              &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                  &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                },
                &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                  &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                },
              },
              &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
              &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                  &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                    { # Configuration for a single speaker in a multi-speaker setup.
                      &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                      &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                        &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                          &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                        },
                      },
                    },
                  ],
                },
                &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                  &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                    &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                  },
                },
              },
              &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                &quot;A String&quot;,
              ],
              &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
              &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
              },
              &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
              &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
            },
            &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
          },
          &quot;metrics&quot;: [ # Required. The metrics used for evaluation.
            { # The metric used for running evaluations.
              &quot;aggregationMetrics&quot;: [ # Optional. The aggregation metrics to use.
                &quot;A String&quot;,
              ],
              &quot;bleuSpec&quot;: { # Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1. # Spec for bleu metric.
                &quot;useEffectiveOrder&quot;: True or False, # Optional. Whether to use_effective_order to compute bleu score.
              },
              &quot;customCodeExecutionSpec&quot;: { # Specificies a metric that is populated by evaluating user-defined Python code. # Spec for Custom Code Execution metric.
                &quot;evaluationFunction&quot;: &quot;A String&quot;, # Required. Python function. Expected user to define the following function, e.g.: def evaluate(instance: dict[str, Any]) -&gt; float: Please include this function signature in the code snippet. Instance is the evaluation instance, any fields populated in the instance are available to the function as instance[field_name]. Example: Example input: ``` instance= EvaluationInstance( response=EvaluationInstance.InstanceData(text=&quot;The answer is 4.&quot;), reference=EvaluationInstance.InstanceData(text=&quot;4&quot;) ) ``` Example converted input: ``` { &#x27;response&#x27;: {&#x27;text&#x27;: &#x27;The answer is 4.&#x27;}, &#x27;reference&#x27;: {&#x27;text&#x27;: &#x27;4&#x27;} } ``` Example python function: ``` def evaluate(instance: dict[str, Any]) -&gt; float: if instance&#x27;response&#x27; == instance&#x27;reference&#x27;: return 1.0 return 0.0 ```
              },
              &quot;exactMatchSpec&quot;: { # Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0. # Spec for exact match metric.
              },
              &quot;llmBasedMetricSpec&quot;: { # Specification for an LLM based metric. # Spec for an LLM based metric.
                &quot;additionalConfig&quot;: { # Optional. Optional additional configuration for the metric.
                  &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                },
                &quot;judgeAutoraterConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Optional configuration for the judge LLM (Autorater).
                  &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
                  &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
                  &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
                    &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
                    &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
                    &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
                    &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
                    &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                      &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                      &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                        &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                      },
                      &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
                    },
                    &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
                    &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
                    &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
                    &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                      &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
                    },
                    &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
                    &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
                    &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
                    &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
                    &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                      &quot;A String&quot;,
                    ],
                    &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                      &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                      &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                        # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                      ],
                      &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                      &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                        &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                      },
                      &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                      &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                        &quot;A String&quot;,
                      ],
                      &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                      &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                      &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                      &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                      &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                      &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                      &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                      &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                      &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                      &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                      &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                      &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                      &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                      &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                        &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                      },
                      &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                        &quot;A String&quot;,
                      ],
                      &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                      &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                        &quot;A String&quot;,
                      ],
                      &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                      &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
                    },
                    &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                      &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                        &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                      },
                      &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                        &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                      },
                    },
                    &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
                    &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                      &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                      &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                        &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                          { # Configuration for a single speaker in a multi-speaker setup.
                            &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                            &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                              &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                                &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                              },
                            },
                          },
                        ],
                      },
                      &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                        &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                          &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                        },
                      },
                    },
                    &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                      &quot;A String&quot;,
                    ],
                    &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
                    &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                      &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                      &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
                    },
                    &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
                    &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
                  },
                  &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
                },
                &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Template for the prompt sent to the judge model.
                &quot;predefinedRubricGenerationSpec&quot;: { # The spec for a pre-defined metric. # Dynamically generate rubrics using a predefined spec.
                  &quot;metricSpecName&quot;: &quot;A String&quot;, # Required. The name of a pre-defined metric, such as &quot;instruction_following_v1&quot; or &quot;text_quality_v1&quot;.
                  &quot;metricSpecParameters&quot;: { # Optional. The parameters needed to run the pre-defined metric.
                    &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                  },
                },
                &quot;rubricGenerationSpec&quot;: { # Specification for how rubrics should be generated. # Dynamically generate rubrics using this specification.
                  &quot;modelConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Configuration for the model used in rubric generation. Configs including sampling count and base model can be specified here. Flipping is not supported for rubric generation.
                    &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
                    &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
                    &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
                      &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
                      &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
                      &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
                      &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
                      &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                        &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                        &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                          &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                        },
                        &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
                      },
                      &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
                      &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
                      &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
                      &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                        &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
                      },
                      &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
                      &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
                      &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
                      &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
                      &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                        &quot;A String&quot;,
                      ],
                      &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                        &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                        &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                          # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                        ],
                        &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                        &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                          &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                        },
                        &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                        &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                          &quot;A String&quot;,
                        ],
                        &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                        &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                        &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                        &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                        &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                        &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                        &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                        &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                        &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                        &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                        &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                        &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                        &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                        &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                          &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                        },
                        &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                          &quot;A String&quot;,
                        ],
                        &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                        &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                          &quot;A String&quot;,
                        ],
                        &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                        &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
                      },
                      &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                        &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                          &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                        },
                        &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                          &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                        },
                      },
                      &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
                      &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                        &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                        &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                          &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                            { # Configuration for a single speaker in a multi-speaker setup.
                              &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                              &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                                &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                                  &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                                },
                              },
                            },
                          ],
                        },
                        &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                          &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                            &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                          },
                        },
                      },
                      &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                        &quot;A String&quot;,
                      ],
                      &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
                      &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                        &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                        &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
                      },
                      &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
                      &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
                    },
                    &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
                  },
                  &quot;promptTemplate&quot;: &quot;A String&quot;, # Template for the prompt used to generate rubrics. The details should be updated based on the most-recent recipe requirements.
                  &quot;rubricContentType&quot;: &quot;A String&quot;, # The type of rubric content to be generated.
                  &quot;rubricTypeOntology&quot;: [ # Optional. An optional, pre-defined list of allowed types for generated rubrics. If this field is provided, it implies `include_rubric_type` should be true, and the generated rubric types should be chosen from this ontology.
                    &quot;A String&quot;,
                  ],
                },
                &quot;rubricGroupKey&quot;: &quot;A String&quot;, # Use a pre-defined group of rubrics associated with the input. Refers to a key in the rubric_groups map of EvaluationInstance.
                &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for the judge model.
              },
              &quot;pairwiseMetricSpec&quot;: { # Spec for pairwise metric. # Spec for pairwise metric.
                &quot;baselineResponseFieldName&quot;: &quot;A String&quot;, # Optional. The field name of the baseline response.
                &quot;candidateResponseFieldName&quot;: &quot;A String&quot;, # Optional. The field name of the candidate response.
                &quot;customOutputFormatConfig&quot;: { # Spec for custom output format configuration. # Optional. CustomOutputFormatConfig allows customization of metric output. When this config is set, the default output is replaced with the raw output string. If a custom format is chosen, the `pairwise_choice` and `explanation` fields in the corresponding metric result will be empty.
                  &quot;returnRawOutput&quot;: True or False, # Optional. Whether to return raw output.
                },
                &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Metric prompt template for pairwise metric.
                &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for pairwise metric.
              },
              &quot;pointwiseMetricSpec&quot;: { # Spec for pointwise metric. # Spec for pointwise metric.
                &quot;customOutputFormatConfig&quot;: { # Spec for custom output format configuration. # Optional. CustomOutputFormatConfig allows customization of metric output. By default, metrics return a score and explanation. When this config is set, the default output is replaced with either: - The raw output string. - A parsed output based on a user-defined schema. If a custom format is chosen, the `score` and `explanation` fields in the corresponding metric result will be empty.
                  &quot;returnRawOutput&quot;: True or False, # Optional. Whether to return raw output.
                },
                &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Metric prompt template for pointwise metric.
                &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for pointwise metric.
              },
              &quot;predefinedMetricSpec&quot;: { # The spec for a pre-defined metric. # The spec for a pre-defined metric.
                &quot;metricSpecName&quot;: &quot;A String&quot;, # Required. The name of a pre-defined metric, such as &quot;instruction_following_v1&quot; or &quot;text_quality_v1&quot;.
                &quot;metricSpecParameters&quot;: { # Optional. The parameters needed to run the pre-defined metric.
                  &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                },
              },
              &quot;rougeSpec&quot;: { # Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1. # Spec for rouge metric.
                &quot;rougeType&quot;: &quot;A String&quot;, # Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.
                &quot;splitSummaries&quot;: True or False, # Optional. Whether to split summaries while using rougeLsum.
                &quot;useStemmer&quot;: True or False, # Optional. Whether to use stemmer to compute rouge score.
              },
            },
          ],
          &quot;outputConfig&quot;: { # Config for evaluation output. # Required. Config for evaluation output.
            &quot;gcsDestination&quot;: { # The Google Cloud Storage location where the output is to be written to. # Cloud storage destination for evaluation output.
              &quot;outputUriPrefix&quot;: &quot;A String&quot;, # Required. Google Cloud Storage URI to output directory. If the uri doesn&#x27;t end with &#x27;/&#x27;, a &#x27;/&#x27; will be automatically appended. The directory is created if it doesn&#x27;t exist.
            },
          },
        },
        &quot;exportLastCheckpointOnly&quot;: True or False, # Optional. If set to true, disable intermediate checkpoints for SFT and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for SFT. Default is false.
        &quot;hyperParameters&quot;: { # Hyperparameters for SFT. # Optional. Hyperparameters for SFT.
          &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for tuning.
          &quot;batchSize&quot;: &quot;A String&quot;, # Optional. Batch size for tuning. This feature is only available for open source models.
          &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
          &quot;learningRate&quot;: 3.14, # Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.
          &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.
        },
        &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
        &quot;tuningMode&quot;: &quot;A String&quot;, # Tuning mode.
        &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
      },
      &quot;tunedModel&quot;: { # The Model Registry Model and Online Prediction Endpoint associated with this TuningJob. # Output only. The tuned model resources associated with this TuningJob.
        &quot;checkpoints&quot;: [ # Output only. The checkpoints associated with this TunedModel. This field is only populated for tuning jobs that enable intermediate checkpoints.
          { # TunedModelCheckpoint for the Tuned Model of a Tuning Job.
            &quot;checkpointId&quot;: &quot;A String&quot;, # The ID of the checkpoint.
            &quot;endpoint&quot;: &quot;A String&quot;, # The Endpoint resource name that the checkpoint is deployed to. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.
            &quot;epoch&quot;: &quot;A String&quot;, # The epoch of the checkpoint.
            &quot;step&quot;: &quot;A String&quot;, # The step of the checkpoint.
          },
        ],
        &quot;endpoint&quot;: &quot;A String&quot;, # Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.
        &quot;model&quot;: &quot;A String&quot;, # Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}@{version_id}` When tuning from a base model, the version ID will be 1. For continuous tuning, if the provided tuned_model_display_name is set and different from parent model&#x27;s display name, the tuned model will have a new parent model with version 1. Otherwise the version id will be incremented by 1 from the last version ID in the parent model. E.g., `projects/{project}/locations/{location}/models/{model}@{last_version_id + 1}`
      },
      &quot;tunedModelDisplayName&quot;: &quot;A String&quot;, # Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters. For continuous tuning, tuned_model_display_name will by default use the same display name as the pre-tuned model. If a new display name is provided, the tuning job will create a new model instead of a new version.
      &quot;tuningDataStats&quot;: { # The tuning data statistic values for TuningJob. # Output only. The tuning data statistics associated with this TuningJob.
        &quot;distillationDataStats&quot;: { # Statistics computed for datasets used for distillation. # Output only. Statistics for distillation.
          &quot;trainingDatasetStats&quot;: { # Statistics computed over a tuning dataset. # Output only. Statistics computed for the training dataset.
            &quot;droppedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
              &quot;A String&quot;,
            ],
            &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `dropped_example_indices`, the user-facing reason why the example was dropped.
              &quot;A String&quot;,
            ],
            &quot;totalBillableCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of billable characters in the tuning dataset.
            &quot;totalTuningCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of tuning characters in the tuning dataset.
            &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
            &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
            &quot;userDatasetExamples&quot;: [ # Output only. Sample user messages in the training dataset uri.
              { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
                &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                  { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                    &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                      &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                      &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                    },
                    &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                      &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                      &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                    },
                    &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                      &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                      &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                        &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                      },
                      &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                      &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                    },
                    &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                      &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                      &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                      &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                        { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                          &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                            &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                            &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                            &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                          },
                          &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                            &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                            &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                            &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                          },
                        },
                      ],
                      &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                        &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                      },
                    },
                    &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                      &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                    &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                    &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                    &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                      &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                      &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                      &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                    },
                  },
                ],
                &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
              },
            ],
            &quot;userInputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user input tokens.
              &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
                { # Dataset bucket used to create a histogram for the distribution given a population of values.
                  &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
                  &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                  &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
                },
              ],
              &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
              &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
              &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
              &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
              &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
              &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
              &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
            },
            &quot;userMessagePerExampleDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the messages per example.
              &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
                { # Dataset bucket used to create a histogram for the distribution given a population of values.
                  &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
                  &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                  &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
                },
              ],
              &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
              &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
              &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
              &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
              &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
              &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
              &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
            },
            &quot;userOutputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user output tokens.
              &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
                { # Dataset bucket used to create a histogram for the distribution given a population of values.
                  &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
                  &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                  &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
                },
              ],
              &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
              &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
              &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
              &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
              &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
              &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
              &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
            },
          },
        },
        &quot;preferenceOptimizationDataStats&quot;: { # Statistics computed for datasets used for preference optimization. # Output only. Statistics for preference optimization.
          &quot;droppedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
            &quot;A String&quot;,
          ],
          &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `dropped_example_indices`, the user-facing reason why the example was dropped.
            &quot;A String&quot;,
          ],
          &quot;scoreVariancePerExampleDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for scores variance per example.
            &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
              { # Dataset bucket used to create a histogram for the distribution given a population of values.
                &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
                &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
              },
            ],
            &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
            &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
            &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
            &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
            &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
            &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
            &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
          },
          &quot;scoresDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for scores.
            &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
              { # Dataset bucket used to create a histogram for the distribution given a population of values.
                &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
                &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
              },
            ],
            &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
            &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
            &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
            &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
            &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
            &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
            &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
          },
          &quot;totalBillableTokenCount&quot;: &quot;A String&quot;, # Output only. Number of billable tokens in the tuning dataset.
          &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
          &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
          &quot;userDatasetExamples&quot;: [ # Output only. Sample user examples in the training dataset.
            { # Input example for preference optimization.
              &quot;completions&quot;: [ # List of completions for a given prompt.
                { # Completion and its preference score.
                  &quot;completion&quot;: { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message. # Single turn completion for the given prompt.
                    &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                      { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                        &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                          &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                          &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                        },
                        &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                          &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                          &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                        },
                        &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                          &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                        &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                          &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                            &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                          },
                          &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                          &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                        },
                        &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                          &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                          &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                          &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                            { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                              &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                                &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                                &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                              },
                              &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                                &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                                &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                              },
                            },
                          ],
                          &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                            &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                          },
                        },
                        &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                          &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                        &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                        &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                        &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                        &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                          &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                          &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                          &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                        },
                      },
                    ],
                    &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
                  },
                  &quot;score&quot;: 3.14, # The score for the given completion.
                },
              ],
              &quot;contents&quot;: [ # Multi-turn contents that represents the Prompt.
                { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
                  &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                    { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                      &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                        &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                        &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                      },
                      &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                        &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                        &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                      },
                      &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                        &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                      &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                        &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                          &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                        },
                        &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                        &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                      },
                      &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                        &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                        &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                        &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                          { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                            &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                              &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                              &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                              &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                            },
                            &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                              &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                              &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                              &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                            },
                          },
                        ],
                        &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                          &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                        },
                      },
                      &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                        &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                      &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                      &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                      &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                      &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                        &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                        &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                        &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                      },
                    },
                  ],
                  &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
                },
              ],
            },
          ],
          &quot;userInputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user input tokens.
            &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
              { # Dataset bucket used to create a histogram for the distribution given a population of values.
                &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
                &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
              },
            ],
            &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
            &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
            &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
            &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
            &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
            &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
            &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
          },
          &quot;userOutputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user output tokens.
            &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
              { # Dataset bucket used to create a histogram for the distribution given a population of values.
                &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
                &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
              },
            ],
            &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
            &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
            &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
            &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
            &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
            &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
            &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
          },
        },
        &quot;supervisedTuningDataStats&quot;: { # Tuning data statistics for Supervised Tuning. # The SFT Tuning data stats.
          &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `truncated_example_indices`, the user-facing reason why the example was dropped.
            &quot;A String&quot;,
          ],
          &quot;totalBillableCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of billable characters in the tuning dataset.
          &quot;totalBillableTokenCount&quot;: &quot;A String&quot;, # Output only. Number of billable tokens in the tuning dataset.
          &quot;totalTruncatedExampleCount&quot;: &quot;A String&quot;, # Output only. The number of examples in the dataset that have been dropped. An example can be dropped for reasons including: too many tokens, contains an invalid image, contains too many images, etc.
          &quot;totalTuningCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of tuning characters in the tuning dataset.
          &quot;truncatedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
            &quot;A String&quot;,
          ],
          &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
          &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
          &quot;userDatasetExamples&quot;: [ # Output only. Sample user messages in the training dataset uri.
            { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
              &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                  &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                    &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                    &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                  },
                  &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                    &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                    &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                  },
                  &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                    &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                    &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                  },
                  &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                    &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                      &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                    },
                    &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                    &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                  },
                  &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                    &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                    &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                    &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                      { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                        &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                          &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                        &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                          &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                      },
                    ],
                    &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                      &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                    },
                  },
                  &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                    &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                    &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                  },
                  &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                  &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                  &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                  &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                    &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                    &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                    &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                  },
                },
              ],
              &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
            },
          ],
          &quot;userInputTokenDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user input tokens.
            &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
            &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
              { # Dataset bucket used to create a histogram for the distribution given a population of values.
                &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
                &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
              },
            ],
            &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
            &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
            &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
            &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
            &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
            &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
            &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
          },
          &quot;userMessagePerExampleDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the messages per example.
            &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
            &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
              { # Dataset bucket used to create a histogram for the distribution given a population of values.
                &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
                &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
              },
            ],
            &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
            &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
            &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
            &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
            &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
            &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
            &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
          },
          &quot;userOutputTokenDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user output tokens.
            &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
            &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
              { # Dataset bucket used to create a histogram for the distribution given a population of values.
                &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
                &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
              },
            ],
            &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
            &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
            &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
            &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
            &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
            &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
            &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
          },
        },
      },
      &quot;tuningJobState&quot;: &quot;A String&quot;, # Output only. The detail state of the tuning job (while the overall `JobState` is running).
      &quot;updateTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob was most recently updated.
      &quot;veoTuningSpec&quot;: { # Tuning Spec for Veo Model Tuning. # Tuning Spec for Veo Tuning.
        &quot;hyperParameters&quot;: { # Hyperparameters for Veo. # Optional. Hyperparameters for Veo.
          &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
          &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
          &quot;tuningTask&quot;: &quot;A String&quot;, # Optional. The tuning task. Either I2V or T2V.
        },
        &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
        &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
      },
    },
  ],
}</pre>
</div>

<div class="method">
    <code class="details" id="list_next">list_next()</code>
  <pre>Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call &#x27;execute()&#x27; on to request the next
          page. Returns None if there are no more items in the collection.
        </pre>
</div>

<div class="method">
    <code class="details" id="optimizePrompt">optimizePrompt(parent, body=None, x__xgafv=None)</code>
  <pre>Optimizes a prompt.

Args:
  parent: string, Required. The resource name of the Location to optimize the prompt in. Format: `projects/{project}/locations/{location}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for GenAiTuningService.OptimizePrompt.
  &quot;content&quot;: { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message. # Required. The content to optimize.
    &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
      { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
        &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
          &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
          &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
        },
        &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
          &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
          &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
        },
        &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
          &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
          &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
        },
        &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
          &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
            &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
          },
          &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
          &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
        },
        &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
          &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
          &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
          &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
            { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
              &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
              },
              &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
              },
            },
          ],
          &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
            &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
          },
        },
        &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
          &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
          &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
        },
        &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
        &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
        &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
        &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
          &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
          &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
          &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
        },
      },
    ],
    &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
  },
  &quot;optimizationTarget&quot;: &quot;A String&quot;, # Optional. The target model to optimize the prompt for.
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for GenAiTuningService.OptimizePrompt
  &quot;content&quot;: { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message. # Output only. The optimized prompt content.
    &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
      { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
        &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
          &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
          &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
        },
        &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
          &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
          &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
        },
        &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
          &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
          &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
        },
        &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
          &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
            &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
          },
          &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
          &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
        },
        &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
          &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
          &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
          &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
            { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
              &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
              },
              &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
              },
            },
          ],
          &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
            &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
          },
        },
        &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
          &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
          &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
        },
        &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
        &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
        &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
        &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
          &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
          &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
          &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
        },
      },
    ],
    &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
  },
}</pre>
</div>

<div class="method">
    <code class="details" id="rebaseTunedModel">rebaseTunedModel(parent, body=None, x__xgafv=None)</code>
  <pre>Rebase a TunedModel.

Args:
  parent: string, Required. The resource name of the Location into which to rebase the Model. Format: `projects/{project}/locations/{location}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for GenAiTuningService.RebaseTunedModel.
  &quot;artifactDestination&quot;: { # The Google Cloud Storage location where the output is to be written to. # Optional. The Google Cloud Storage location to write the artifacts.
    &quot;outputUriPrefix&quot;: &quot;A String&quot;, # Required. Google Cloud Storage URI to output directory. If the uri doesn&#x27;t end with &#x27;/&#x27;, a &#x27;/&#x27; will be automatically appended. The directory is created if it doesn&#x27;t exist.
  },
  &quot;deployToSameEndpoint&quot;: True or False, # Optional. By default, bison to gemini migration will always create new model/endpoint, but for gemini-1.0 to gemini-1.5 migration, we default deploy to the same endpoint. See details in this Section.
  &quot;tunedModelRef&quot;: { # TunedModel Reference for legacy model migration. # Required. TunedModel reference to retrieve the legacy model information.
    &quot;pipelineJob&quot;: &quot;A String&quot;, # Support migration from tuning job list page, from bison model to gemini model.
    &quot;tunedModel&quot;: &quot;A String&quot;, # Support migration from model registry.
    &quot;tuningJob&quot;: &quot;A String&quot;, # Support migration from tuning job list page, from gemini-1.0-pro-002 to 1.5 and above.
  },
  &quot;tuningJob&quot;: { # Represents a TuningJob that runs with Google owned models. # Optional. The TuningJob to be updated. Users can use this TuningJob field to overwrite tuning configs.
    &quot;baseModel&quot;: &quot;A String&quot;, # The base model that is being tuned. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).
    &quot;createTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob was created.
    &quot;customBaseModel&quot;: &quot;A String&quot;, # Optional. The user-provided path to custom model weights. Set this field to tune a custom model. The path must be a Cloud Storage directory that contains the model weights in .safetensors format along with associated model metadata files. If this field is set, the base_model field must still be set to indicate which base model the custom model is derived from. This feature is only available for open source models.
    &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the TuningJob.
    &quot;distillationSpec&quot;: { # Tuning Spec for Distillation. # Tuning Spec for Distillation.
      &quot;baseTeacherModel&quot;: &quot;A String&quot;, # The base teacher model that is being distilled. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).
      &quot;hyperParameters&quot;: { # Hyperparameters for Distillation. # Optional. Hyperparameters for Distillation.
        &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for distillation.
        &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
        &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
      },
      &quot;pipelineRootDirectory&quot;: &quot;A String&quot;, # Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.
      &quot;studentModel&quot;: &quot;A String&quot;, # The student model that is being tuned, e.g., &quot;google/gemma-2b-1.1-it&quot;. Deprecated. Use base_model instead.
      &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
      &quot;tunedTeacherModelSource&quot;: &quot;A String&quot;, # The resource name of the Tuned teacher model. Format: `projects/{project}/locations/{location}/models/{model}`.
      &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.
    },
    &quot;encryptionSpec&quot;: { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key.
      &quot;kmsKeyName&quot;: &quot;A String&quot;, # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
    },
    &quot;endTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob entered any of the following JobStates: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`, `JOB_STATE_EXPIRED`.
    &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. Only populated when job&#x27;s state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`.
      &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
      &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
        {
          &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
        },
      ],
      &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
    },
    &quot;evaluateDatasetRuns&quot;: [ # Output only. Evaluation runs for the Tuning Job.
      { # Evaluate Dataset Run Result for Tuning Job.
        &quot;checkpointId&quot;: &quot;A String&quot;, # Output only. The checkpoint id used in the evaluation run. Only populated when evaluating checkpoints.
        &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # Output only. The error of the evaluation run if any.
          &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
          &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
            {
              &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
            },
          ],
          &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
        },
        &quot;evaluateDatasetResponse&quot;: { # Response in LRO for EvaluationService.EvaluateDataset. # Output only. Results for EvaluationService.EvaluateDataset.
          &quot;aggregationOutput&quot;: { # The aggregation result for the entire dataset and all metrics. # Output only. Aggregation statistics derived from results of EvaluationService.EvaluateDataset.
            &quot;aggregationResults&quot;: [ # One AggregationResult per metric.
              { # The aggregation result for a single metric.
                &quot;aggregationMetric&quot;: &quot;A String&quot;, # Aggregation metric.
                &quot;bleuMetricValue&quot;: { # Bleu metric value for an instance. # Results for bleu metric.
                  &quot;score&quot;: 3.14, # Output only. Bleu score.
                },
                &quot;customCodeExecutionResult&quot;: { # Result for custom code execution metric. # Result for code execution metric.
                  &quot;score&quot;: 3.14, # Output only. Custom code execution score.
                },
                &quot;exactMatchMetricValue&quot;: { # Exact match metric value for an instance. # Results for exact match metric.
                  &quot;score&quot;: 3.14, # Output only. Exact match score.
                },
                &quot;pairwiseMetricResult&quot;: { # Spec for pairwise metric result. # Result for pairwise metric.
                  &quot;customOutput&quot;: { # Spec for custom output. # Output only. Spec for custom output.
                    &quot;rawOutputs&quot;: { # Raw output. # Output only. List of raw output strings.
                      &quot;rawOutput&quot;: [ # Output only. Raw output string.
                        &quot;A String&quot;,
                      ],
                    },
                  },
                  &quot;explanation&quot;: &quot;A String&quot;, # Output only. Explanation for pairwise metric score.
                  &quot;pairwiseChoice&quot;: &quot;A String&quot;, # Output only. Pairwise metric choice.
                },
                &quot;pointwiseMetricResult&quot;: { # Spec for pointwise metric result. # Result for pointwise metric.
                  &quot;customOutput&quot;: { # Spec for custom output. # Output only. Spec for custom output.
                    &quot;rawOutputs&quot;: { # Raw output. # Output only. List of raw output strings.
                      &quot;rawOutput&quot;: [ # Output only. Raw output string.
                        &quot;A String&quot;,
                      ],
                    },
                  },
                  &quot;explanation&quot;: &quot;A String&quot;, # Output only. Explanation for pointwise metric score.
                  &quot;score&quot;: 3.14, # Output only. Pointwise metric score.
                },
                &quot;rougeMetricValue&quot;: { # Rouge metric value for an instance. # Results for rouge metric.
                  &quot;score&quot;: 3.14, # Output only. Rouge score.
                },
              },
            ],
            &quot;dataset&quot;: { # The dataset used for evaluation. # The dataset used for evaluation &amp; aggregation.
              &quot;bigquerySource&quot;: { # The BigQuery location for the input content. # BigQuery source holds the dataset.
                &quot;inputUri&quot;: &quot;A String&quot;, # Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: * BigQuery path. For example: `bq://projectId.bqDatasetId.bqTableId`.
              },
              &quot;gcsSource&quot;: { # The Google Cloud Storage location for the input content. # Cloud storage source holds the dataset. Currently only one Cloud Storage file path is supported.
                &quot;uris&quot;: [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
                  &quot;A String&quot;,
                ],
              },
            },
          },
          &quot;outputInfo&quot;: { # Describes the info for output of EvaluationService.EvaluateDataset. # Output only. Output info for EvaluationService.EvaluateDataset.
            &quot;gcsOutputDirectory&quot;: &quot;A String&quot;, # Output only. The full path of the Cloud Storage directory created, into which the evaluation results and aggregation results are written.
          },
        },
        &quot;operationName&quot;: &quot;A String&quot;, # Output only. The operation ID of the evaluation run. Format: `projects/{project}/locations/{location}/operations/{operation_id}`.
      },
    ],
    &quot;experiment&quot;: &quot;A String&quot;, # Output only. The Experiment associated with this TuningJob.
    &quot;fullFineTuningSpec&quot;: { # Tuning Spec for Full Fine Tuning. # Tuning Spec for Full Fine Tuning.
      &quot;hyperParameters&quot;: { # Hyperparameters for SFT. # Optional. Hyperparameters for Full Fine Tuning.
        &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for tuning.
        &quot;batchSize&quot;: &quot;A String&quot;, # Optional. Batch size for tuning. This feature is only available for open source models.
        &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
        &quot;learningRate&quot;: 3.14, # Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.
        &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.
      },
      &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
      &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    },
    &quot;labels&quot;: { # Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
      &quot;a_key&quot;: &quot;A String&quot;,
    },
    &quot;name&quot;: &quot;A String&quot;, # Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`
    &quot;outputUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to the directory where tuning job outputs are written to. This field is only available and required for open source models.
    &quot;partnerModelTuningSpec&quot;: { # Tuning spec for Partner models. # Tuning Spec for open sourced and third party Partner models.
      &quot;hyperParameters&quot;: { # Hyperparameters for tuning. The accepted hyper_parameters and their valid range of values will differ depending on the base model.
        &quot;a_key&quot;: &quot;&quot;,
      },
      &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
      &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.
    },
    &quot;pipelineJob&quot;: &quot;A String&quot;, # Output only. The resource name of the PipelineJob associated with the TuningJob. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}`.
    &quot;preTunedModel&quot;: { # A pre-tuned model for continuous tuning. # The pre-tuned model for continuous tuning.
      &quot;baseModel&quot;: &quot;A String&quot;, # Output only. The name of the base model this PreTunedModel was tuned from.
      &quot;checkpointId&quot;: &quot;A String&quot;, # Optional. The source checkpoint id. If not specified, the default checkpoint will be used.
      &quot;tunedModelName&quot;: &quot;A String&quot;, # The resource name of the Model. E.g., a model resource name with a specified version id or alias: `projects/{project}/locations/{location}/models/{model}@{version_id}` `projects/{project}/locations/{location}/models/{model}@{alias}` Or, omit the version id to use the default version: `projects/{project}/locations/{location}/models/{model}`
    },
    &quot;preferenceOptimizationSpec&quot;: { # Tuning Spec for Preference Optimization. # Tuning Spec for Preference Optimization.
      &quot;exportLastCheckpointOnly&quot;: True or False, # Optional. If set to true, disable intermediate checkpoints for Preference Optimization and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for Preference Optimization. Default is false.
      &quot;hyperParameters&quot;: { # Hyperparameters for Preference Optimization. # Optional. Hyperparameters for Preference Optimization.
        &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for preference optimization.
        &quot;beta&quot;: 3.14, # Optional. Weight for KL Divergence regularization.
        &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
        &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
      },
      &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.
      &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.
    },
    &quot;satisfiesPzi&quot;: True or False, # Output only. Reserved for future use.
    &quot;satisfiesPzs&quot;: True or False, # Output only. Reserved for future use.
    &quot;serviceAccount&quot;: &quot;A String&quot;, # The service account that the tuningJob workload runs as. If not specified, the Vertex AI Secure Fine-Tuned Service Agent in the project will be used. See https://cloud.google.com/iam/docs/service-agents#vertex-ai-secure-fine-tuning-service-agent Users starting the pipeline must have the `iam.serviceAccounts.actAs` permission on this service account.
    &quot;startTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state.
    &quot;state&quot;: &quot;A String&quot;, # Output only. The detailed state of the job.
    &quot;supervisedTuningSpec&quot;: { # Tuning Spec for Supervised Tuning for first party models. # Tuning Spec for Supervised Fine Tuning.
      &quot;evaluationConfig&quot;: { # Evaluation Config for Tuning Job. # Optional. Evaluation Config for Tuning Job.
        &quot;autoraterConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Autorater config for evaluation.
          &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
          &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
          &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
            &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
            &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
            &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
            &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
            &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
              &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
              &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
              },
              &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
            },
            &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
            &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
            &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
            &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
              &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
            },
            &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
            &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
            &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
            &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
            &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
              &quot;A String&quot;,
            ],
            &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
              &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
              &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                # Object with schema name: GoogleCloudAiplatformV1beta1Schema
              ],
              &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
              &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
              },
              &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
              &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                &quot;A String&quot;,
              ],
              &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
              &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
              &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
              &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
              &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
              &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
              &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
              &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
              &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
              &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
              &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
              &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
              &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
              &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
              },
              &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                &quot;A String&quot;,
              ],
              &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
              &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                &quot;A String&quot;,
              ],
              &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
              &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
            },
            &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
              &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
              },
              &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
              },
            },
            &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
            &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
              &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
              &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                  { # Configuration for a single speaker in a multi-speaker setup.
                    &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                    &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                      &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                        &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                      },
                    },
                  },
                ],
              },
              &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                  &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                },
              },
            },
            &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
              &quot;A String&quot;,
            ],
            &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
            &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
              &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
              &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
            },
            &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
            &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
          },
          &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
        },
        &quot;metrics&quot;: [ # Required. The metrics used for evaluation.
          { # The metric used for running evaluations.
            &quot;aggregationMetrics&quot;: [ # Optional. The aggregation metrics to use.
              &quot;A String&quot;,
            ],
            &quot;bleuSpec&quot;: { # Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1. # Spec for bleu metric.
              &quot;useEffectiveOrder&quot;: True or False, # Optional. Whether to use_effective_order to compute bleu score.
            },
            &quot;customCodeExecutionSpec&quot;: { # Specificies a metric that is populated by evaluating user-defined Python code. # Spec for Custom Code Execution metric.
              &quot;evaluationFunction&quot;: &quot;A String&quot;, # Required. Python function. Expected user to define the following function, e.g.: def evaluate(instance: dict[str, Any]) -&gt; float: Please include this function signature in the code snippet. Instance is the evaluation instance, any fields populated in the instance are available to the function as instance[field_name]. Example: Example input: ``` instance= EvaluationInstance( response=EvaluationInstance.InstanceData(text=&quot;The answer is 4.&quot;), reference=EvaluationInstance.InstanceData(text=&quot;4&quot;) ) ``` Example converted input: ``` { &#x27;response&#x27;: {&#x27;text&#x27;: &#x27;The answer is 4.&#x27;}, &#x27;reference&#x27;: {&#x27;text&#x27;: &#x27;4&#x27;} } ``` Example python function: ``` def evaluate(instance: dict[str, Any]) -&gt; float: if instance&#x27;response&#x27; == instance&#x27;reference&#x27;: return 1.0 return 0.0 ```
            },
            &quot;exactMatchSpec&quot;: { # Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0. # Spec for exact match metric.
            },
            &quot;llmBasedMetricSpec&quot;: { # Specification for an LLM based metric. # Spec for an LLM based metric.
              &quot;additionalConfig&quot;: { # Optional. Optional additional configuration for the metric.
                &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
              },
              &quot;judgeAutoraterConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Optional. Optional configuration for the judge LLM (Autorater).
                &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
                &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
                &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
                  &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
                  &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
                  &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
                  &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
                  &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                    &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                    &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                      &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                    },
                    &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
                  },
                  &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
                  &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
                  &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
                  &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                    &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
                  },
                  &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
                  &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
                  &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
                  &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
                  &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                    &quot;A String&quot;,
                  ],
                  &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                    &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                    &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                      # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    ],
                    &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                    &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                      &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    },
                    &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                    &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                      &quot;A String&quot;,
                    ],
                    &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                    &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                    &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                    &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                    &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                    &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                    &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                    &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                    &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                    &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                    &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                    &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                    &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                    &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                      &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                    },
                    &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                      &quot;A String&quot;,
                    ],
                    &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                    &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                      &quot;A String&quot;,
                    ],
                    &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                    &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
                  },
                  &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                    &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                      &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                    },
                    &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                      &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                    },
                  },
                  &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
                  &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                    &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                    &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                      &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                        { # Configuration for a single speaker in a multi-speaker setup.
                          &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                          &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                            &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                              &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                            },
                          },
                        },
                      ],
                    },
                    &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                      &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                        &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                      },
                    },
                  },
                  &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                    &quot;A String&quot;,
                  ],
                  &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
                  &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                    &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                    &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
                  },
                  &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
                  &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
                },
                &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
              },
              &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Template for the prompt sent to the judge model.
              &quot;predefinedRubricGenerationSpec&quot;: { # The spec for a pre-defined metric. # Dynamically generate rubrics using a predefined spec.
                &quot;metricSpecName&quot;: &quot;A String&quot;, # Required. The name of a pre-defined metric, such as &quot;instruction_following_v1&quot; or &quot;text_quality_v1&quot;.
                &quot;metricSpecParameters&quot;: { # Optional. The parameters needed to run the pre-defined metric.
                  &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                },
              },
              &quot;rubricGenerationSpec&quot;: { # Specification for how rubrics should be generated. # Dynamically generate rubrics using this specification.
                &quot;modelConfig&quot;: { # The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset. # Configuration for the model used in rubric generation. Configs including sampling count and base model can be specified here. Flipping is not supported for rubric generation.
                  &quot;autoraterModel&quot;: &quot;A String&quot;, # Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
                  &quot;flipEnabled&quot;: True or False, # Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.
                  &quot;generationConfig&quot;: { # Configuration for content generation. This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output. # Optional. Configuration options for model generation and outputs.
                    &quot;audioTimestamp&quot;: True or False, # Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
                    &quot;candidateCount&quot;: 42, # Optional. The number of candidate responses to generate. A higher `candidate_count` can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
                    &quot;enableAffectiveDialog&quot;: True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
                    &quot;frequencyPenalty&quot;: 3.14, # Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
                    &quot;imageConfig&quot;: { # Configuration for image generation. This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people. # Optional. Config for image generation features.
                      &quot;aspectRatio&quot;: &quot;A String&quot;, # Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: &quot;1:1&quot; &quot;2:3&quot;, &quot;3:2&quot; &quot;3:4&quot;, &quot;4:3&quot; &quot;4:5&quot;, &quot;5:4&quot; &quot;9:16&quot;, &quot;16:9&quot; &quot;21:9&quot;
                      &quot;imageOutputOptions&quot;: { # The image output format for generated images. # Optional. The image output format for generated images.
                        &quot;compressionQuality&quot;: 42, # Optional. The compression quality of the output image.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Optional. The image format that the output should be saved as.
                      },
                      &quot;personGeneration&quot;: &quot;A String&quot;, # Optional. Controls whether the model can generate people.
                    },
                    &quot;logprobs&quot;: 42, # Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
                    &quot;maxOutputTokens&quot;: 42, # Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
                    &quot;mediaResolution&quot;: &quot;A String&quot;, # Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
                    &quot;modelConfig&quot;: { # Config for model selection. # Optional. Config for model selection.
                      &quot;featureSelectionPreference&quot;: &quot;A String&quot;, # Required. Feature selection preference.
                    },
                    &quot;presencePenalty&quot;: 3.14, # Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
                    &quot;responseJsonSchema&quot;: &quot;&quot;, # Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to `application/json`.
                    &quot;responseLogprobs&quot;: True or False, # Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model&#x27;s confidence in its own output and for debugging.
                    &quot;responseMimeType&quot;: &quot;A String&quot;, # Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include &#x27;text/plain&#x27; (default) and &#x27;application/json&#x27;. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
                    &quot;responseModalities&quot;: [ # Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to `[TEXT, IMAGE]`, the response will include both text and an image.
                      &quot;A String&quot;,
                    ],
                    &quot;responseSchema&quot;: { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Lets you to specify a schema for the model&#x27;s response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema) object. When this field is set, you must also set the `response_mime_type` to `application/json`.
                      &quot;additionalProperties&quot;: &quot;&quot;, # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                      &quot;anyOf&quot;: [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                        # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                      ],
                      &quot;default&quot;: &quot;&quot;, # Optional. Default value of the data.
                      &quot;defs&quot;: { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                        &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                      },
                      &quot;description&quot;: &quot;A String&quot;, # Optional. The description of the data.
                      &quot;enum&quot;: [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:[&quot;EAST&quot;, NORTH&quot;, &quot;SOUTH&quot;, &quot;WEST&quot;]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:[&quot;101&quot;, &quot;201&quot;, &quot;301&quot;]}
                        &quot;A String&quot;,
                      ],
                      &quot;example&quot;: &quot;&quot;, # Optional. Example of the object. Will only populated when the object is the root.
                      &quot;format&quot;: &quot;A String&quot;, # Optional. The format of the data. Supported formats: for NUMBER type: &quot;float&quot;, &quot;double&quot; for INTEGER type: &quot;int32&quot;, &quot;int64&quot; for STRING type: &quot;email&quot;, &quot;byte&quot;, etc
                      &quot;items&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                      &quot;maxItems&quot;: &quot;A String&quot;, # Optional. Maximum number of the elements for Type.ARRAY.
                      &quot;maxLength&quot;: &quot;A String&quot;, # Optional. Maximum length of the Type.STRING
                      &quot;maxProperties&quot;: &quot;A String&quot;, # Optional. Maximum number of the properties for Type.OBJECT.
                      &quot;maximum&quot;: 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                      &quot;minItems&quot;: &quot;A String&quot;, # Optional. Minimum number of the elements for Type.ARRAY.
                      &quot;minLength&quot;: &quot;A String&quot;, # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                      &quot;minProperties&quot;: &quot;A String&quot;, # Optional. Minimum number of the properties for Type.OBJECT.
                      &quot;minimum&quot;: 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                      &quot;nullable&quot;: True or False, # Optional. Indicates if the value may be null.
                      &quot;pattern&quot;: &quot;A String&quot;, # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                      &quot;properties&quot;: { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                        &quot;a_key&quot;: # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                      },
                      &quot;propertyOrdering&quot;: [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                        &quot;A String&quot;,
                      ],
                      &quot;ref&quot;: &quot;A String&quot;, # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named &quot;Pet&quot;: type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the &quot;pet&quot; property is a reference to the schema node named &quot;Pet&quot;. See details in https://json-schema.org/understanding-json-schema/structuring
                      &quot;required&quot;: [ # Optional. Required properties of Type.OBJECT.
                        &quot;A String&quot;,
                      ],
                      &quot;title&quot;: &quot;A String&quot;, # Optional. The title of the Schema.
                      &quot;type&quot;: &quot;A String&quot;, # Optional. The type of the data.
                    },
                    &quot;routingConfig&quot;: { # The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name. # Optional. Routing configuration.
                      &quot;autoMode&quot;: { # The configuration for automated routing. When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # In this mode, the model is selected automatically based on the content of the request.
                        &quot;modelRoutingPreference&quot;: &quot;A String&quot;, # The model routing preference.
                      },
                      &quot;manualMode&quot;: { # The configuration for manual routing. When manual routing is specified, the model will be selected based on the model name provided. # In this mode, the model is specified manually.
                        &quot;modelName&quot;: &quot;A String&quot;, # The name of the model to use. Only public LLM models are accepted.
                      },
                    },
                    &quot;seed&quot;: 42, # Optional. A seed for the random number generator. By setting a seed, you can make the model&#x27;s output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it&#x27;s not a guaranteed absolute deterministic behavior. This is different from parameters like `temperature`, which control the *level* of randomness. `seed` ensures that the &quot;random&quot; choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
                    &quot;speechConfig&quot;: { # Configuration for speech generation. # Optional. The speech generation config.
                      &quot;languageCode&quot;: &quot;A String&quot;, # Optional. The language code (ISO 639-1) for the speech synthesis.
                      &quot;multiSpeakerVoiceConfig&quot;: { # Configuration for a multi-speaker text-to-speech request. # The configuration for a multi-speaker text-to-speech request. This field is mutually exclusive with `voice_config`.
                        &quot;speakerVoiceConfigs&quot;: [ # Required. A list of configurations for the voices of the speakers. Exactly two speaker voice configurations must be provided.
                          { # Configuration for a single speaker in a multi-speaker setup.
                            &quot;speaker&quot;: &quot;A String&quot;, # Required. The name of the speaker. This should be the same as the speaker name used in the prompt.
                            &quot;voiceConfig&quot;: { # Configuration for a voice. # Required. The configuration for the voice of this speaker.
                              &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                                &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                              },
                            },
                          },
                        ],
                      },
                      &quot;voiceConfig&quot;: { # Configuration for a voice. # The configuration for the voice to use.
                        &quot;prebuiltVoiceConfig&quot;: { # Configuration for a prebuilt voice. # The configuration for a prebuilt voice.
                          &quot;voiceName&quot;: &quot;A String&quot;, # The name of the prebuilt voice to use.
                        },
                      },
                    },
                    &quot;stopSequences&quot;: [ # Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use [&quot;\n&quot;, &quot;###&quot;] to stop generation at a new line or a specific marker.
                      &quot;A String&quot;,
                    ],
                    &quot;temperature&quot;: 3.14, # Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
                    &quot;thinkingConfig&quot;: { # Configuration for the model&#x27;s thinking features. &quot;Thinking&quot; is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response. # Optional. Configuration for thinking features. An error will be returned if this field is set for models that don&#x27;t support thinking.
                      &quot;includeThoughts&quot;: True or False, # Optional. If true, the model will include its thoughts in the response. &quot;Thoughts&quot; are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model&#x27;s reasoning process and help with debugging. If this is true, thoughts are returned only when available.
                      &quot;thinkingBudget&quot;: 42, # Optional. The token budget for the model&#x27;s thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
                    },
                    &quot;topK&quot;: 3.14, # Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a `top_k` of 40 means the model will choose the next word from the 40 most likely words.
                    &quot;topP&quot;: 3.14, # Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least `top_p`. This helps generate more diverse and less repetitive responses. For example, a `top_p` of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It&#x27;s recommended to adjust either temperature or `top_p`, but not both.
                  },
                  &quot;samplingCount&quot;: 42, # Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
                },
                &quot;promptTemplate&quot;: &quot;A String&quot;, # Template for the prompt used to generate rubrics. The details should be updated based on the most-recent recipe requirements.
                &quot;rubricContentType&quot;: &quot;A String&quot;, # The type of rubric content to be generated.
                &quot;rubricTypeOntology&quot;: [ # Optional. An optional, pre-defined list of allowed types for generated rubrics. If this field is provided, it implies `include_rubric_type` should be true, and the generated rubric types should be chosen from this ontology.
                  &quot;A String&quot;,
                ],
              },
              &quot;rubricGroupKey&quot;: &quot;A String&quot;, # Use a pre-defined group of rubrics associated with the input. Refers to a key in the rubric_groups map of EvaluationInstance.
              &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for the judge model.
            },
            &quot;pairwiseMetricSpec&quot;: { # Spec for pairwise metric. # Spec for pairwise metric.
              &quot;baselineResponseFieldName&quot;: &quot;A String&quot;, # Optional. The field name of the baseline response.
              &quot;candidateResponseFieldName&quot;: &quot;A String&quot;, # Optional. The field name of the candidate response.
              &quot;customOutputFormatConfig&quot;: { # Spec for custom output format configuration. # Optional. CustomOutputFormatConfig allows customization of metric output. When this config is set, the default output is replaced with the raw output string. If a custom format is chosen, the `pairwise_choice` and `explanation` fields in the corresponding metric result will be empty.
                &quot;returnRawOutput&quot;: True or False, # Optional. Whether to return raw output.
              },
              &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Metric prompt template for pairwise metric.
              &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for pairwise metric.
            },
            &quot;pointwiseMetricSpec&quot;: { # Spec for pointwise metric. # Spec for pointwise metric.
              &quot;customOutputFormatConfig&quot;: { # Spec for custom output format configuration. # Optional. CustomOutputFormatConfig allows customization of metric output. By default, metrics return a score and explanation. When this config is set, the default output is replaced with either: - The raw output string. - A parsed output based on a user-defined schema. If a custom format is chosen, the `score` and `explanation` fields in the corresponding metric result will be empty.
                &quot;returnRawOutput&quot;: True or False, # Optional. Whether to return raw output.
              },
              &quot;metricPromptTemplate&quot;: &quot;A String&quot;, # Required. Metric prompt template for pointwise metric.
              &quot;systemInstruction&quot;: &quot;A String&quot;, # Optional. System instructions for pointwise metric.
            },
            &quot;predefinedMetricSpec&quot;: { # The spec for a pre-defined metric. # The spec for a pre-defined metric.
              &quot;metricSpecName&quot;: &quot;A String&quot;, # Required. The name of a pre-defined metric, such as &quot;instruction_following_v1&quot; or &quot;text_quality_v1&quot;.
              &quot;metricSpecParameters&quot;: { # Optional. The parameters needed to run the pre-defined metric.
                &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
              },
            },
            &quot;rougeSpec&quot;: { # Spec for rouge score metric - calculates the recall of n-grams in prediction as compared to reference - returns a score ranging between 0 and 1. # Spec for rouge metric.
              &quot;rougeType&quot;: &quot;A String&quot;, # Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.
              &quot;splitSummaries&quot;: True or False, # Optional. Whether to split summaries while using rougeLsum.
              &quot;useStemmer&quot;: True or False, # Optional. Whether to use stemmer to compute rouge score.
            },
          },
        ],
        &quot;outputConfig&quot;: { # Config for evaluation output. # Required. Config for evaluation output.
          &quot;gcsDestination&quot;: { # The Google Cloud Storage location where the output is to be written to. # Cloud storage destination for evaluation output.
            &quot;outputUriPrefix&quot;: &quot;A String&quot;, # Required. Google Cloud Storage URI to output directory. If the uri doesn&#x27;t end with &#x27;/&#x27;, a &#x27;/&#x27; will be automatically appended. The directory is created if it doesn&#x27;t exist.
          },
        },
      },
      &quot;exportLastCheckpointOnly&quot;: True or False, # Optional. If set to true, disable intermediate checkpoints for SFT and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for SFT. Default is false.
      &quot;hyperParameters&quot;: { # Hyperparameters for SFT. # Optional. Hyperparameters for SFT.
        &quot;adapterSize&quot;: &quot;A String&quot;, # Optional. Adapter size for tuning.
        &quot;batchSize&quot;: &quot;A String&quot;, # Optional. Batch size for tuning. This feature is only available for open source models.
        &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
        &quot;learningRate&quot;: 3.14, # Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.
        &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.
      },
      &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
      &quot;tuningMode&quot;: &quot;A String&quot;, # Tuning mode.
      &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    },
    &quot;tunedModel&quot;: { # The Model Registry Model and Online Prediction Endpoint associated with this TuningJob. # Output only. The tuned model resources associated with this TuningJob.
      &quot;checkpoints&quot;: [ # Output only. The checkpoints associated with this TunedModel. This field is only populated for tuning jobs that enable intermediate checkpoints.
        { # TunedModelCheckpoint for the Tuned Model of a Tuning Job.
          &quot;checkpointId&quot;: &quot;A String&quot;, # The ID of the checkpoint.
          &quot;endpoint&quot;: &quot;A String&quot;, # The Endpoint resource name that the checkpoint is deployed to. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.
          &quot;epoch&quot;: &quot;A String&quot;, # The epoch of the checkpoint.
          &quot;step&quot;: &quot;A String&quot;, # The step of the checkpoint.
        },
      ],
      &quot;endpoint&quot;: &quot;A String&quot;, # Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.
      &quot;model&quot;: &quot;A String&quot;, # Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}@{version_id}` When tuning from a base model, the version ID will be 1. For continuous tuning, if the provided tuned_model_display_name is set and different from parent model&#x27;s display name, the tuned model will have a new parent model with version 1. Otherwise the version id will be incremented by 1 from the last version ID in the parent model. E.g., `projects/{project}/locations/{location}/models/{model}@{last_version_id + 1}`
    },
    &quot;tunedModelDisplayName&quot;: &quot;A String&quot;, # Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters. For continuous tuning, tuned_model_display_name will by default use the same display name as the pre-tuned model. If a new display name is provided, the tuning job will create a new model instead of a new version.
    &quot;tuningDataStats&quot;: { # The tuning data statistic values for TuningJob. # Output only. The tuning data statistics associated with this TuningJob.
      &quot;distillationDataStats&quot;: { # Statistics computed for datasets used for distillation. # Output only. Statistics for distillation.
        &quot;trainingDatasetStats&quot;: { # Statistics computed over a tuning dataset. # Output only. Statistics computed for the training dataset.
          &quot;droppedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
            &quot;A String&quot;,
          ],
          &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `dropped_example_indices`, the user-facing reason why the example was dropped.
            &quot;A String&quot;,
          ],
          &quot;totalBillableCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of billable characters in the tuning dataset.
          &quot;totalTuningCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of tuning characters in the tuning dataset.
          &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
          &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
          &quot;userDatasetExamples&quot;: [ # Output only. Sample user messages in the training dataset uri.
            { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
              &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                  &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                    &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                    &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                  },
                  &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                    &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                    &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                  },
                  &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                    &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                    &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                  },
                  &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                    &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                      &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                    },
                    &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                    &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                  },
                  &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                    &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                    &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                    &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                      { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                        &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                          &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                        &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                          &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                          &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                          &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                        },
                      },
                    ],
                    &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                      &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                    },
                  },
                  &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                    &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                    &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                    &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                  },
                  &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                  &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                  &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                  &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                    &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                    &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                    &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                  },
                },
              ],
              &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
            },
          ],
          &quot;userInputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user input tokens.
            &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
              { # Dataset bucket used to create a histogram for the distribution given a population of values.
                &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
                &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
              },
            ],
            &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
            &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
            &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
            &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
            &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
            &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
            &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
          },
          &quot;userMessagePerExampleDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the messages per example.
            &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
              { # Dataset bucket used to create a histogram for the distribution given a population of values.
                &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
                &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
              },
            ],
            &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
            &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
            &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
            &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
            &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
            &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
            &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
          },
          &quot;userOutputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user output tokens.
            &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
              { # Dataset bucket used to create a histogram for the distribution given a population of values.
                &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
                &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
                &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
              },
            ],
            &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
            &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
            &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
            &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
            &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
            &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
            &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
          },
        },
      },
      &quot;preferenceOptimizationDataStats&quot;: { # Statistics computed for datasets used for preference optimization. # Output only. Statistics for preference optimization.
        &quot;droppedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
          &quot;A String&quot;,
        ],
        &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `dropped_example_indices`, the user-facing reason why the example was dropped.
          &quot;A String&quot;,
        ],
        &quot;scoreVariancePerExampleDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for scores variance per example.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
        &quot;scoresDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for scores.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
        &quot;totalBillableTokenCount&quot;: &quot;A String&quot;, # Output only. Number of billable tokens in the tuning dataset.
        &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
        &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
        &quot;userDatasetExamples&quot;: [ # Output only. Sample user examples in the training dataset.
          { # Input example for preference optimization.
            &quot;completions&quot;: [ # List of completions for a given prompt.
              { # Completion and its preference score.
                &quot;completion&quot;: { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message. # Single turn completion for the given prompt.
                  &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                    { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                      &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                        &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                        &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                      },
                      &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                        &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                        &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                      },
                      &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                        &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                      &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                        &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                          &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                        },
                        &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                        &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                      },
                      &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                        &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                        &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                        &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                          { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                            &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                              &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                              &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                              &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                            },
                            &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                              &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                              &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                              &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                            },
                          },
                        ],
                        &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                          &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                        },
                      },
                      &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                        &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                      &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                      &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                      &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                      &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                        &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                        &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                        &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                      },
                    },
                  ],
                  &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
                },
                &quot;score&quot;: 3.14, # The score for the given completion.
              },
            ],
            &quot;contents&quot;: [ # Multi-turn contents that represents the Prompt.
              { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
                &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
                  { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                    &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                      &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                      &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                    },
                    &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                      &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                      &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                    },
                    &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                      &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                      &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                        &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                      },
                      &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                      &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                    },
                    &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                      &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                      &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                      &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                        { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                          &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                            &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                            &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                            &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                          },
                          &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                            &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                            &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                            &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                          },
                        },
                      ],
                      &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                        &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                      },
                    },
                    &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                      &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                      &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                      &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                    },
                    &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                    &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                    &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                    &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                      &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                      &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                      &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                    },
                  },
                ],
                &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
              },
            ],
          },
        ],
        &quot;userInputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user input tokens.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
        &quot;userOutputTokenDistribution&quot;: { # Distribution computed over a tuning dataset. # Output only. Dataset distributions for the user output tokens.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: &quot;A String&quot;, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: 3.14, # Output only. Sum of a given population of values.
        },
      },
      &quot;supervisedTuningDataStats&quot;: { # Tuning data statistics for Supervised Tuning. # The SFT Tuning data stats.
        &quot;droppedExampleReasons&quot;: [ # Output only. For each index in `truncated_example_indices`, the user-facing reason why the example was dropped.
          &quot;A String&quot;,
        ],
        &quot;totalBillableCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of billable characters in the tuning dataset.
        &quot;totalBillableTokenCount&quot;: &quot;A String&quot;, # Output only. Number of billable tokens in the tuning dataset.
        &quot;totalTruncatedExampleCount&quot;: &quot;A String&quot;, # Output only. The number of examples in the dataset that have been dropped. An example can be dropped for reasons including: too many tokens, contains an invalid image, contains too many images, etc.
        &quot;totalTuningCharacterCount&quot;: &quot;A String&quot;, # Output only. Number of tuning characters in the tuning dataset.
        &quot;truncatedExampleIndices&quot;: [ # Output only. A partial sample of the indices (starting from 1) of the dropped examples.
          &quot;A String&quot;,
        ],
        &quot;tuningDatasetExampleCount&quot;: &quot;A String&quot;, # Output only. Number of examples in the tuning dataset.
        &quot;tuningStepCount&quot;: &quot;A String&quot;, # Output only. Number of tuning steps for this Tuning Job.
        &quot;userDatasetExamples&quot;: [ # Output only. Sample user messages in the training dataset uri.
          { # The structured data content of a message. A Content message contains a `role` field, which indicates the producer of the content, and a `parts` field, which contains the multi-part data of the message.
            &quot;parts&quot;: [ # Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.
              { # A datatype containing media that is part of a multi-part Content message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. For media types that are not text, `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                &quot;codeExecutionResult&quot;: { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. The result of executing the ExecutableCode.
                  &quot;outcome&quot;: &quot;A String&quot;, # Required. Outcome of the code execution.
                  &quot;output&quot;: &quot;A String&quot;, # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                },
                &quot;executableCode&quot;: { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is intended to be executed.
                  &quot;code&quot;: &quot;A String&quot;, # Required. The code to be executed.
                  &quot;language&quot;: &quot;A String&quot;, # Required. Programming language of the `code`.
                },
                &quot;fileData&quot;: { # URI-based data. A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage. # Optional. The URI-based data of the part. This can be used to include files from Google Cloud Storage.
                  &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                  &quot;fileUri&quot;: &quot;A String&quot;, # Required. The URI of the file in Google Cloud Storage.
                  &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                },
                &quot;functionCall&quot;: { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted function call returned from the model. This contains the name of the function to call and the arguments to pass to the function.
                  &quot;args&quot;: { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                    &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                  },
                  &quot;id&quot;: &quot;A String&quot;, # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                  &quot;name&quot;: &quot;A String&quot;, # Optional. The name of the function to call. Matches [FunctionDeclaration.name].
                },
                &quot;functionResponse&quot;: { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # Optional. The result of a function call. This is used to provide the model with the result of a function call that it predicted.
                  &quot;id&quot;: &quot;A String&quot;, # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                  &quot;name&quot;: &quot;A String&quot;, # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                  &quot;parts&quot;: [ # Optional. Ordered `Parts` that constitute a function response. Parts may have different IANA MIME types.
                    { # A datatype containing media that is part of a `FunctionResponse` message. A `FunctionResponsePart` consists of data which has an associated datatype. A `FunctionResponsePart` can only contain one of the accepted types in `FunctionResponsePart.data`. A `FunctionResponsePart` must have a fixed IANA MIME type identifying the type and subtype of the media if the `inline_data` field is filled with raw bytes.
                      &quot;fileData&quot;: { # URI based data for function response. # URI based data.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                        &quot;fileUri&quot;: &quot;A String&quot;, # Required. URI.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                      &quot;inlineData&quot;: { # Raw media bytes for function response. Text should not be sent as raw bytes, use the &#x27;text&#x27; field. # Inline media bytes.
                        &quot;data&quot;: &quot;A String&quot;, # Required. Raw bytes.
                        &quot;displayName&quot;: &quot;A String&quot;, # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                        &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                      },
                    },
                  ],
                  &quot;response&quot;: { # Required. The function response in JSON object format. Use &quot;output&quot; key to specify function output and &quot;error&quot; key to specify error details (if any). If &quot;output&quot; and &quot;error&quot; keys are not specified, then whole &quot;response&quot; is treated as function output.
                    &quot;a_key&quot;: &quot;&quot;, # Properties of the object.
                  },
                },
                &quot;inlineData&quot;: { # A content blob. A Blob contains data of a specific media type. It is used to represent images, audio, and video. # Optional. The inline data content of the part. This can be used to include images, audio, or video in a request.
                  &quot;data&quot;: &quot;A String&quot;, # Required. The raw bytes of the data.
                  &quot;displayName&quot;: &quot;A String&quot;, # Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in `PromptMessage` for prompt management. It is used in the Gemini calls only when server-side tools (`code_execution`, `google_search`, and `url_context`) are enabled.
                  &quot;mimeType&quot;: &quot;A String&quot;, # Required. The IANA standard MIME type of the source data.
                },
                &quot;text&quot;: &quot;A String&quot;, # Optional. The text content of the part.
                &quot;thought&quot;: True or False, # Optional. Indicates whether the `part` represents the model&#x27;s thought process or reasoning.
                &quot;thoughtSignature&quot;: &quot;A String&quot;, # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                &quot;videoMetadata&quot;: { # Provides metadata for a video, including the start and end offsets for clipping and the frame rate. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                  &quot;endOffset&quot;: &quot;A String&quot;, # Optional. The end offset of the video.
                  &quot;fps&quot;: 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value is 1.0. The valid range is (0.0, 24.0].
                  &quot;startOffset&quot;: &quot;A String&quot;, # Optional. The start offset of the video.
                },
              },
            ],
            &quot;role&quot;: &quot;A String&quot;, # Optional. The producer of the content. Must be either &#x27;user&#x27; or &#x27;model&#x27;. If not set, the service will default to &#x27;user&#x27;.
          },
        ],
        &quot;userInputTokenDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user input tokens.
          &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
        },
        &quot;userMessagePerExampleDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the messages per example.
          &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
        },
        &quot;userOutputTokenDistribution&quot;: { # Dataset distribution for Supervised Tuning. # Output only. Dataset distributions for the user output tokens.
          &quot;billableSum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values that are billable.
          &quot;buckets&quot;: [ # Output only. Defines the histogram bucket.
            { # Dataset bucket used to create a histogram for the distribution given a population of values.
              &quot;count&quot;: 3.14, # Output only. Number of values in the bucket.
              &quot;left&quot;: 3.14, # Output only. Left bound of the bucket.
              &quot;right&quot;: 3.14, # Output only. Right bound of the bucket.
            },
          ],
          &quot;max&quot;: 3.14, # Output only. The maximum of the population values.
          &quot;mean&quot;: 3.14, # Output only. The arithmetic mean of the values in the population.
          &quot;median&quot;: 3.14, # Output only. The median of the values in the population.
          &quot;min&quot;: 3.14, # Output only. The minimum of the population values.
          &quot;p5&quot;: 3.14, # Output only. The 5th percentile of the values in the population.
          &quot;p95&quot;: 3.14, # Output only. The 95th percentile of the values in the population.
          &quot;sum&quot;: &quot;A String&quot;, # Output only. Sum of a given population of values.
        },
      },
    },
    &quot;tuningJobState&quot;: &quot;A String&quot;, # Output only. The detail state of the tuning job (while the overall `JobState` is running).
    &quot;updateTime&quot;: &quot;A String&quot;, # Output only. Time when the TuningJob was most recently updated.
    &quot;veoTuningSpec&quot;: { # Tuning Spec for Veo Model Tuning. # Tuning Spec for Veo Tuning.
      &quot;hyperParameters&quot;: { # Hyperparameters for Veo. # Optional. Hyperparameters for Veo.
        &quot;epochCount&quot;: &quot;A String&quot;, # Optional. Number of complete passes the model makes over the entire training dataset during training.
        &quot;learningRateMultiplier&quot;: 3.14, # Optional. Multiplier for adjusting the default learning rate.
        &quot;tuningTask&quot;: &quot;A String&quot;, # Optional. The tuning task. Either I2V or T2V.
      },
      &quot;trainingDatasetUri&quot;: &quot;A String&quot;, # Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
      &quot;validationDatasetUri&quot;: &quot;A String&quot;, # Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.
    },
  },
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  &quot;done&quot;: True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  &quot;error&quot;: { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    &quot;code&quot;: 42, # The status code, which should be an enum value of google.rpc.Code.
    &quot;details&quot;: [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
      },
    ],
    &quot;message&quot;: &quot;A String&quot;, # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  &quot;metadata&quot;: { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
  },
  &quot;name&quot;: &quot;A String&quot;, # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  &quot;response&quot;: { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    &quot;a_key&quot;: &quot;&quot;, # Properties of the object. Contains field @type with type URL.
  },
}</pre>
</div>

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